A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
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paper_title: A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis

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The research in fault diagnosis for rolling element bearings has been attracting great interest in recent years. This is because bearings are frequently failed and the consequence could cause unexpected breakdown of machines. When a fault is occurring in a bearing, periodic impulses can be revealed in its generated vibration frequency spectrum. Different types of bearing faults will lead to impulses appearing at different periodic intervals. In order to extract the periodic impulses effectively, numerous techniques have been developed to reveal bearing fault characteristic frequencies. In this study, an adaptive varying-scale morphological analysis in time domain is proposed. This analysis can be applied to one-dimensional signal by defining different lengths of the structure elements based on the local peaks of the impulses. The analysis has been first validated by simulated impulses, and then by real bearing vibration signals embedded with faulty impulses caused by an inner race defect and an outer race...
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paper_title: Incipient fault information determination for rolling element bearing based on synchronous averaging reassigned wavelet scalogram

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Abstract As incipient fault of rotating machinery is weak and interfered by noise, it is difficult for characteristic information determination and incipient classification. In this research, a new method is put forward on rolling element bearing (REB) incipient impact information using synchronous averaging reassigned wavelet scalogram (SARWS) according to time–frequency analysis. Firstly, multi-cycle signal is processed by continuous wavelet transform. Then, time frequency distribution for every working cycle of vibration signal is obtained by wavelet scalogram (WS) based on time domain information. Thirdly, reassigned wavelet scalogram (RWS) for every working cycle of REB can be calculated. In the end, synchronous averaging is applied on RWS, which can effectively reduce noise interference and identify the weak fault information. Both simulated signals and real vibration signals collected from REB of rotating machinery are used to verify this proposed method. Analyzed results show that the proposed method is effective for REB incipient weak fault classification.
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paper_title: Health Condition Monitoring and Early Fault Diagnosis of Bearings Using SDF and Intrinsic Characteristic-Scale Decomposition

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Early fault diagnosis is crucial to reduce the machine downtime. This paper presents a novel method based on symbolic dynamic filtering (SDF) for early fault detection and intrinsic characteristic-scale decomposition (ICD) for fault type recognition. SDF is first applied to extract the fault feature for depicting bearing performance degradation. Then, a fault alarm is triggered using cumulative sum. Finally, the extracted abnormal signal is decomposed by the ICD method, and the kurtosis method is used to select a principal product component that contains most fault information for fault detection. The real life experimental results validate the effectiveness of the proposed method in early detection of bearing fault and fault diagnosis in comparison with Fourier transform, Hilbert envelope spectrum, original local mean decomposition and spectral kurtosis.
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paper_title: Dual serial vortex-induced energy harvesting system for enhanced energy harvesting

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This paper presents a novel dual serial vortex-induced vibration energy harvesting system for enhanced energy harvesting. It consists of two identical cantilever-based piezoelectric vortex-induced vibration energy harvesters, which are successively installed in one plane (which is paralleled with the wind flow direction) of the wind tunnel. The Lattice Boltzmann method is employed to predict the strength of vortex-induced vibrations and the pressure distribution around the circular cylinders of the harvesters. The numerical results qualitatively explain the influence of the space distance on the energy harvesting performance of the presented system. Experimental results verify the numerical analysis and demonstrate a higher energy harvesting efficiency of the presented system over its traditional single harvester. In detail, experimental results indicate that the effective wind speed range and the output power area of a coupled harvester in the presented system can be as many as 2.67 times and 6.79 times ...
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paper_title: Statistical Spectral Analysis for Fault Diagnosis of Rotating Machines

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Condition-based monitoring of rotating machines requires robust features for accurate fault diagnosis, which is indeed directly linked to the quality of the features extracted from the signals. This is especially true for vibration data, whose quasi-stationary nature implies that the quality of frequency domain extracted features depends on the signal-to-noise ratio (SNR) condition, operating condition variations, and data segmentation. This paper presents a novel statistical spectral analysis, which leads to highly robust fault diagnosis with poor SNR conditions, different time-window segmentation, and different operating conditions. The amplitudes of spectral contents of the quasi-stationary time vibration signals are sorted and transformed into statistical spectral images. The sort operation leads to the knowledge of the empirical cumulative distribution function (ECDF) of the amplitudes of each frequency band. The ECDF provides a robust statistical information of the distribution of the amplitude under different SNR and operating conditions. Statistical metrics have been adopted for fault classification, by using the ECDFs obtained from the spectral images as fault features. By applying simple statistical metrics, it is possible to achieve fault diagnosis without classifier training, saving both time and computational costs. The proposed algorithm has been tested using a vibration data benchmark: comparison with state-of-the-art fault diagnosis algorithms shows promising results.
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paper_title: Review for order reduction based on proper orthogonal decomposition and outlooks of applications in mechanical systems

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Abstract This paper presents a review of proper orthogonal decomposition (POD) methods for order reduction in a variety of research areas. The historical development and basic mathematical formulation of the POD method are introduced. POD for parametric dynamic systems is introduced, and a physical interpretation of the POD approach based on the proper orthogonal modes (POMs) is presented. The equivalence between POD and three other order reduction methods is discussed: the first alternative method is singular value decomposition (SVD), the second is principal component analysis (PCA), and the third is Karhunen-Loeve decomposition (KLD). A classification of POD methods is described based on the parameter adaptation and sampling. Actual applications of POD methods for order reduction in engineering systems are illustrated. Finally, outlooks on the use of POD methods in high-dimensional nonlinear dynamic systems are presented in more detail to provide direct guidance for researchers in various areas of engineering.
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paper_title: HVSRMS localization formula and localization law: Localization diagnosis of a ball bearing outer ring fault

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Abstract The angle position of the outer ring fault has a significant influence on the residual life and the operation performance of ball bearing. Therefore, the realization of the localization diagnosis of the outer ring fault is of great importance to the bearing performance degradation assessment and the life prediction. In this paper, a novel localization diagnosis method, named horizontal–vertical synchronized root mean square (HVSRMS) localization law and localization formula, is developed to diagnose the angle position of outer ring fault accurately. Firstly, based on the established nonlinear contact model of the bearing system, the detailed change law of static contact force when the angle position of the outer ring fault is on both sides of 270° is studied, which lays a theoretical foundation for the discovery of localization law. Subsequently, the vibration acceleration signal and dynamic contact force of the outer ring fault are synchronously analyzed through theoretical research, and the new feature for localization diagnosis is presented as: the prime impact direction of horizontal vibration acceleration (IPDoHVA). Therefore, the outer ring fault localization law of ball bearing is proposed. On this basis, the HVSRMS localization formula is improved by combining it with the localization law. Thus the one-to-one mapping relationship between HVSRMS and outer ring fault angle position is determined. Finally, experiments are carried out to validate the accuracy and feasibility of the proposed localization law and localization formula. The research results provide a new approach and method for localization diagnosis and the residual life prediction of a defective bearing.
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paper_title: Enhanced broadband piezoelectric energy harvesting using rotatable magnets

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We investigate a magnetically coupled nonlinear piezoelectric energy harvester by altering the angular orientation of its external magnets for enhanced broadband frequency response. Electromechanical equations describing the nonlinear dynamic behavior include an experimentally identified polynomial for the transverse magnetic force that depends on magnet angle. Up- and down-sweep harmonic excitation tests are performed at constant acceleration over the range of 0–25 Hz. Very good agreement is observed between the numerical and experimental open-circuit voltage output frequency response curves. The nonlinear energy harvester proposed in this work can cover the broad low-frequency range of 4–22 Hz by changing the magnet orientation.
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paper_title: Early fault diagnosis of rolling bearings based on hierarchical symbol dynamic entropy and binary tree support vector machine

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Abstract Early fault diagnosis of rolling bearings is crucial to operating and maintenance cost reduction of the equipment with bearings. This paper aims to propose a novel early fault feature extraction method based on the proposed hierarchical symbol dynamic entropy (HSDE) and the binary tree support vector machine (BT-SVM). Multiscale symbolic dynamic entropy (MSDE) has been recently proposed to characterize the dynamical behavior of time series. MSDE has several merits comparing with multiscale sample entropy (MSE) and multiscale permutation entropy (MPE), such as high computational efficiency and robustness to noise. However, MSDE only utilizes the fault information in the low frequency components and consequently the fault information hidden in the high frequency components is discarded. To address this shortcoming, a new method, namely HSDE, is proposed to extract the fault information in the high frequency components. Then, the BT-SVM is utilized to automatically complete the fault type identification. The effectiveness of the proposed method is validated using simulated and experimental vibration signals. Meanwhile, a comparison is conducted between MPE, hierarchical permutation entropy (HPE), MSE, hierarchical sample entropy (HSE), MSDE and HSDE. Results show that the proposed method performs best to recognize the early fault types of rolling bearings.
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paper_title: Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine

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Abstract Diagnosis of bearings generally plays an important role in fault diagnosis of mechanical system, and machine learning has been a promising tool in this field. In many real applications of bearings fault diagnosis, the data tend to be online imbalanced, which means, the number of fault data is much less than the normal data while they are all collected in online sequential way. Suffering from this problem, many traditional diagnosis methods will get low accuracy of fault data which acts as the minority class in the collected bearing data. To address this problem, an online sequential prediction method for imbalanced fault diagnosis problem is proposed based on extreme learning machine. This method introduces the principal curve and granulation division to simulate the flow distribution and overall distribution characteristics of fault data, respectively. Then a confident over-sampling and under-sampling process is proposed to establish the initial offline diagnosis model. In online stage, the obtained granules and principal curves are rebuilt on the bearing data which are arrived in sequence, and after the over-sampling and under-sampling process, the balanced sample set is formed to update the diagnosis model dynamically. A theoretical analysis is provided and proves that, even existing information loss, the proposed method has lower bound of the model reliability. Simulation experiments are conducted on IMS bearing data and CWRU bearing data. The comparative results demonstrate that the proposed method can improve the fault diagnosis accuracy with better effectiveness and robustness than other algorithms.
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paper_title: A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy

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Abstract The fault diagnosis of planetary gearboxes is crucial to reduce the maintenance costs and economic losses. This paper proposes a novel fault diagnosis method based on adaptive multi-scale morphological filter (AMMF) and modified hierarchical permutation entropy (MHPE) to identify the different health conditions of planetary gearboxes. In this method, AMMF is firstly adopted to remove the fault-unrelated components and enhance the fault characteristics. Second, MHPE is utilized to extract the fault features from the denoised vibration signals. Third, Laplacian score (LS) approach is employed to refine the fault features. In the end, the obtained features are fed into the binary tree support vector machine (BT-SVM) to accomplish the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault categories of planetary gearboxes.
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paper_title: Fault diagnosis of reciprocating compressor valve with the method integrating acoustic emission signal and simulated valve motion

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Abstract This paper proposes a method of diagnosing faults in reciprocating compressor valves using the acoustic emission signal coupled with the simulated valve motion. The actual working condition of a valve can be obtained by analyzing the acoustic emission signal in the crank angle domain and the valve movement can be predicted by simulating the valve motion. The exact opening and closing locations of a normal valve, provided by the simulated valve motion, can be used as references for the valve fault diagnosis. The typical valve faults are diagnosed to validate the feasibility and accuracy of the proposed method. The experimental results indicate that this method can easily distinguish the normal valve, valve flutter and valve delayed closing conditions. The characteristic locations of the opening and closing of the suction and discharge valves can be clearly identified in the waveform of the acoustic emission signal and the simulated valve motion.
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paper_title: Dynamic modeling of gearbox faults: A review

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Abstract Gearbox is widely used in industrial and military applications. Due to high service load, harsh operating conditions or inevitable fatigue, faults may develop in gears. If the gear faults cannot be detected early, the health will continue to degrade, perhaps causing heavy economic loss or even catastrophe. Early fault detection and diagnosis allows properly scheduled shutdowns to prevent catastrophic failure and consequently result in a safer operation and higher cost reduction. Recently, many studies have been done to develop gearbox dynamic models with faults aiming to understand gear fault generation mechanism and then develop effective fault detection and diagnosis methods. This paper focuses on dynamics based gearbox fault modeling, detection and diagnosis. State-of-art and challenges are reviewed and discussed. This detailed literature review limits research results to the following fundamental yet key aspects: gear mesh stiffness evaluation, gearbox damage modeling and fault diagnosis techniques, gearbox transmission path modeling and method validation. In the end, a summary and some research prospects are presented.
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paper_title: A Patent Analysis of Prognostics and Health Management (PHM) Innovations for Electrical Systems

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The use of reliability assessment and prediction in product design, manufacture, and operation has been an evolutionary process. Physics-based modeling and root-cause analysis of failure sites, failure modes, and failure mechanisms have proven to be effective in the prevention and fault detection of product failures over the past decades. Nevertheless, prognostics and health management (PHM) is now the focus of recent activity as noted by the amount of books, articles, and patents. This three-part review aims to give a comprehensive overview of PHM patents from three aspects: PHM for electrical systems, PHM for mechanical systems, and general PHM methodologies. This series of survey papers reviews the history of PHM research in industry and its current status based on U.S. patents from 2000 to 2015; discusses the application of PHM in the design, manufacture, and deployment of equipment in various industries; and presents some of the key research questions which remain to be addressed, with particular attention on the results reported in the last decade. In this part, 114 PHM patents for electrical systems are covered and comprehensively reviewed from the perspective of seven electrical appliances. Several phenomena are summarized through the investigation of U.S. PHM patents from 2000 to 2015. The proportion of the PHM patents for systems increased much more than for components. Compared with single sensor, sensor networks have achieved more applications so that objects are monitored more comprehensively and accurately. In addition, online assessment with on-board solutions is gradually replacing offline assessment of products using downloaded data to realize timely monitoring and troubleshooting.
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paper_title: A review of vibration-based techniques for helicopter transmission diagnostics

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Over the past 25 years, much research has been devoted to the development of Health and Usage Monitoring (HUM) systems for rotorcraft gearbox and drivetrain components. The promise of HUM systems is the ability to provide accurate information regarding the condition of various flight critical components. This paper reviews the state of the art in vibration-based helicopter transmission diagnostics. The development of various damage detection techniques is discussed from a historical perspective, and the ability of these techniques to detect damage in helicopter transmissions is reviewed. Emerging research trends suggest that improvements in signal processing, sensor development and individual-tooth mesh waveform modelling could improve the performance of current and future helicopter transmission diagnostics.
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paper_title: Current Noise Cancellation for Bearing Fault Diagnosis Using Time Shifting

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The stator current of a typical induction motor involves the supply fundamental and its harmonics existing before and after the presence of a bearing defect, and much of the information they contain is not related to the bearing defect. In this sense, they are basically noise to bearing fault diagnosis problem. This paper develops a current noise cancellation method using time shifting. Current residue is obtained by adding digital current signal to its own sample-delayed representation as an antinoise component. The amount of the sample delay is only dependent upon the supply frequency and the sampling rate. This amount is set to eliminate the supply fundamental and its odd multiple harmonics. After obtaining the current residue, through the spectral analysis of the envelope of the current residue, the characteristic fault frequencies for a defective bearing can be revealed. To show the superiority, the developed method is experimentally compared with three of the current-based methods in the literature. Experimental results for the defects in the outer raceway, the inner raceway, and the ball of a bearing verify the merits and effectiveness of the developed method.
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paper_title: Fault detection analysis in rolling element bearing: A review

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Abstract Rolling element bearings are very critical components of rotating machines and the presence of defects in the bearing may lead to failure of machines. Hence, early identification of such defects along with the severity of damage under operating condition of the bearing may avoid malfunctioning and breakdown of machines. Defective bearings are source of vibration and these vibration signals can be used to assess the faulty bearings. This article presents the brief review of recent trends in research on bearing defects, sources of vibration and vibration measurement techniques in time domain, frequency domain and time frequency domain.
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paper_title: A fault diagnosis method for planetary gearboxes under non-stationary working conditions using improved Vold-Kalman filter and multi-scale sample entropy

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Abstract This paper presents a novel signal processing scheme by combining an improved Vold-Kalman filter and the multi-scale sample entropy (IVKF-MSSE) for planetary gearboxes under non-stationary working conditions. In this scheme, we propose a method based on the characteristic frequency ratio (CFR) to select the VKF bandwidth. First, a CFR is adopted to select a VKF bandwidth with the largest CFR value as the optimal VKF bandwidth. Second, IVKF is used to extract fault-induced information under time-varying speed conditions. Because an optimal bandwidth is used in VKF, the feature extraction capability of VKF is enhanced. Then, the MSSE is applied to extract gearbox fault features. After that, the Laplacian score (LS) approach is introduced to refine the fault features by sorting the scale factors. At the end, the selected features are fed into the least square support vector machine (LSSVM) for effective fault pattern identification. Simulation and experimental vibration signals are employed to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the auto-regressive AR-MSSE, VKF-MSSE and EEMD-MSSE in identifying fault types of planetary gearboxes.
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paper_title: A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection

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Abstract Health condition identification of planetary gearboxes is crucial to reduce the downtime and maximize productivity. This paper aims to develop a novel fault diagnosis method based on modified multi-scale symbolic dynamic entropy (MMSDE) and minimum redundancy maximum relevance (mRMR) to identify the different health conditions of planetary gearbox. MMSDE is proposed to quantify the regularity of time series, which can assess the dynamical characteristics over a range of scales. MMSDE has obvious advantages in the detection of dynamical changes and computation efficiency. Then, the mRMR approach is introduced to refine the fault features. Lastly, the obtained new features are fed into the least square support vector machine (LSSVM) to complete the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault types of planetary gearboxes.
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paper_title: Entropy Based Fault Classification Using the Case Western Reserve University Data: A Benchmark Study

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Fault diagnosis of bearings using classification techniques plays an important role in industrial applications, and, hence, has received increasing attention. Recently, significant efforts have been made to develop various methods for bearing fault classification and the application of Case Western Reserve University (CWRU) data for validation has become a standard reference to test the fault classification algorithms. However, a systematic research for evaluating bearing fault classification performance using the CWRU data is still lacking. This paper aims to provide a comprehensive benchmark analysis of the CWRU data using various entropy and classification methods. The main contribution of this paper is applying entropy-based fault classification methods to establish a benchmark analysis of entire CWRU datasets, aiming to provide a proper assessment of any new classification methods. Recommendations are provided for the selection of the CWRU data to aid in testing new fault classification algorithms, which will enable the researches to develop and evaluate various diagnostic algorithms. In the end, the comparison results and discussion are reported as a useful baseline for future research.
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paper_title: Techniques Developed for Fault Diagnosis of Long-range Running Ball Screw Drive Machine to Evaluate Lubrication Condition

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Abstract This study focuses on the acquisitions of vibration and torque signals of a ball screw drive system in upward-downward reciprocating motion and data processing techniques in order to identify the longest running distance under normal operation without lubricant replenishment. The Fast Fourier transform (FFT) spectra were used to determine the approximate frequency ranges of vibrations due to the friction and wear arising in the upper and lower nuts of the duel ball-screw design. Fractal theory was applied to the vibration signals to determine fractal dimension (D) and topothesy (G) values varying with distance. G is an efficient parameter for evaluating approximate lubrication degradation. A running distance of 90–100 km was roughly estimated to be the starting point of lubrication degradation. The vibration signals associated with the upper and lower nuts were classified using support vector machine (SVM) to evaluate their accuracy. The highest accuracy (>95%) was obtained for the upper nut, for which the predicted running distance was near 100 km. The running distance before required lubricant replenishment was thus determined to be within a range of 90–100 km. The upper nut experienced the highest friction and wear; its effect on the lower nut is also discussed.
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paper_title: The Entropy Algorithm and Its Variants in the Fault Diagnosis of Rotating Machinery: A Review

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Rotating machines have been widely used in industrial engineering. The fault diagnosis of rotating machines plays a vital important role to reduce the catastrophic failures and heavy economic loss. However, the measured vibration signal of rotating machinery often represents non-linear and non-stationary characteristics, resulting in difficulty in the fault feature extraction. As a statistical measure, entropy can quantify the complexity and detect dynamic change through taking into account the non-linear behavior of time series. Therefore, entropy can be served as a promising tool to extract the dynamic characteristics of rotating machines. Recently, many studies have applied entropy in fault diagnosis of rotating machinery. This paper aims to investigate the applications of entropy for the fault characteristics extraction of rotating machines. First, various entropy methods are briefly introduced. Its foundation, application, and some improvements are described and discussed. The review is divided into eight parts: Shannon entropy, Renyi entropy, approximate entropy, sample entropy, fuzzy entropy, permutation entropy, and other entropy methods. In each part, we will review the applications using the original entropy method and the improved entropy methods, respectively. In the end, a summary and some research prospects are given.
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paper_title: Health Assessment of Rotating Machinery Using a Rotary Encoder

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In this paper, a systematic framework is established for health assessment of rotating machinery using a rotary encoder. In this framework, spectral quadratic weighting is first developed to convert encoder signals into acceleration series. Comb filtering is then introduced to remove the interferences resulting from drive/load variations. Finally, an adaptive denoising scheme based on Gini index is proposed to enhance the impulses caused by mechanical defects. The simulation and experimental results show that the proposed method is sensitive to incipient faults, and offers a promising tool for health assessment of rotating machinery.
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paper_title: Artificial intelligence for fault diagnosis of rotating machinery: A review

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Abstract Fault diagnosis of rotating machinery plays a significant role for the reliability and safety of modern industrial systems. As an emerging field in industrial applications and an effective solution for fault recognition, artificial intelligence (AI) techniques have been receiving increasing attention from academia and industry. However, great challenges are met by the AI methods under the different real operating conditions. This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications. A brief introduction of different AI algorithms is presented first, including the following methods: k -nearest neighbour, naive Bayes, support vector machine, artificial neural network and deep learning. Then, a broad literature survey of these AI algorithms in industrial applications is given. Finally, the advantages, limitations, practical implications of different AI algorithms, as well as some new research trends, are discussed.
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paper_title: Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation

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Abstract Continuous online monitoring of rotating machines is necessary to assess real-time health conditions so as to enable early detection of operation problems and thus reduce the possibility of downtime. Rolling element bearings are crucial parts of many machines and there has been an increasing demand to find effective and reliable health monitoring technique and advanced signal processing to detect and diagnose the size and location of incipient defects. Condition monitoring of rolling element bearings, comprises four main stages which are, statistical analysis, fault diagnostics, defect size calculation, and prognostics. In this paper the effect of defect size, operating speed, and loading conditions on statistical parameters of acoustic emission (AE) signals, using design of experiment method (DOE), have been investigated to select the most sensitive parameters for diagnosing incipient faults and defect growth on rolling element bearings. A modified and effective signal processing algorithm is designed to diagnose localized defects on rolling element bearings components under different operating speeds, loadings, and defect sizes. The algorithm is based on optimizing the ratio of Kurtosis and Shannon entropy to obtain the optimal band pass filter utilizing wavelet packet transform (WPT) and envelope detection. Results show the superiority of the developed algorithm and its effectiveness in extracting bearing characteristic frequencies from the raw acoustic emission signals masked by background noise under different operating conditions. To experimentally measure the defect size on rolling element bearings using acoustic emission technique, the proposed method along with spectrum of squared Hilbert transform are performed under different rotating speeds, loading conditions, and defect sizes to measure the time difference between the double AE impulses. Measurement results show the power of the proposed method for experimentally measuring size of different fault shapes using acoustic emission signals.
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paper_title: Quantitative and Localization Diagnosis of a Defective Ball Bearing Based on Vertical–Horizontal Synchronization Signal Analysis

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A novel method is proposed for quantitative and localization diagnosis of a bearing outer ring defect in this paper. This method aims to distinguish these defects with different angular positions and different sizes based on the natural multiples of the ball angular spacing. Defects that differ in size by natural multiples of the ball angular spacing will have the same time interval, which may lead to misdiagnosis. Distinguishing such defects accurately is the goal for some industries. Therefore, the synchronized signal in the vertical and horizontal directions of the bearing systems with different angular positions and different size defects are quantitatively analyzed based on the presented five indices to explore the mechanism and method of distinguishing these defects. The simulation and experimental results show that the vertical–horizontal synchronized root mean square (VHSRMS) index can distinguish such defects effectively, and the defect angle position can be diagnosed using the VHSRMS–1 . More importantly, the VHS analysis method expands the location of the outer ring defect to 265–275° in quantitative diagnosis and realizes the localization diagnosis of the defect angle position. The research results provide a new approach and method for quantitative and localization diagnosis of a defective bearing.
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paper_title: Weak Fault Feature Extraction of Rolling Bearings Using Local Mean Decomposition-Based Multilayer Hybrid Denoising

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Extraction of the weak fault features under strong background noise is crucial to early fault diagnosis in bearings. A new method called local mean decomposition (LMD)-based multilayer hybrid denoising (LMD-MHD) is proposed for signal denoising in this paper. LMD is a novel self-adaptive time-frequency analysis method. It can decompose the signal into a set of product functions (PFs), and is thus particularly suitable for processing of multicomponent amplitude-modulated and frequency-modulated signals. The first filtering layer of LMD-MHD is to use a multiple criteria decision to select the effective PF components. The second filtering layer is to use the wavelet threshold denoising (WTD) as the prefilter of singular value decomposition (SVD) implementation, which enables the preserved singular values to consist of the most important information of the PFs. The final denoising layer of LMD-MHD is to use SVD to extract the most important principal features from Hankel matrix of the PFs. The order of effective ranks of the Hankel matrix is determined by the number of main frequencies in the fast Fourier transformation (FFT) result of the signal. The filtering is finished based on the reconstructed signal from the decomposition result of SVD. The results of experimental analysis on the simulation signals and vibration signal collected from rolling bearings indicate that LMD-MHD is effective for extracting the weak fault features and performs well for bearing fault diagnosis.
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paper_title: Low Complexity Motor Current Signature Analysis Using Sub-Nyquist Strategy With Reduced Data Length

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Motor current signature analysis requires high sampling rate and a large number of data samples to detect faults at light loading conditions. However, it would be interesting to investigate the alternative methods to bring down these requirements using the principle of sparse signal processing. In this paper, a low-complexity fault detection algorithm based on the sub-Nyquist sampling of the analytic current signal has been proposed and compared to other similar algorithms. The acquired current data is first converted into an analytic signal using a low-cost time-shift-based method. The processing of the analytic current signal has been performed at a rate 20 times lower than the Nyquist criterion using multicoset sampling technique. The computational complexity has been reduced further by partially reconstructing the spectrum only for the regions of interest. The regions of interest were found out from the estimated supply frequency and the armature current magnitude. The supply frequency has been estimated from the transformed analytic current signal. The algorithm has been tested for broken rotor bar (BRB), eccentricity, and broken bearing faults, using the recorded data from a 22-kW induction motor drive at various loading conditions in the laboratory. In addition, the BRB fault has also been tested for motors with 1.5- and 3.7-kW ratings. A receiver operating characteristic curve has been generated to test the performance of the algorithm. The proposed algorithm uses only a single current sensor to acquire one of the armature phase currents.
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paper_title: A review on empirical mode decomposition in fault diagnosis of rotating machinery

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Abstract Rotating machinery covers a broad range of mechanical equipment and plays a significant role in industrial applications. It generally operates under tough working environment and is therefore subject to faults, which could be detected and diagnosed by using signal processing techniques. Empirical mode decomposition (EMD) is one of the most powerful signal processing techniques and has been extensively studied and widely applied in fault diagnosis of rotating machinery. Numerous publications on the use of EMD for fault diagnosis have appeared in academic journals, conference proceedings and technical reports. This paper attempts to survey and summarize the recent research and development of EMD in fault diagnosis of rotating machinery, providing comprehensive references for researchers concerning with this topic and helping them identify further research topics. First, the EMD method is briefly introduced, the usefulness of the method is illustrated and the problems and the corresponding solutions are listed. Then, recent applications of EMD to fault diagnosis of rotating machinery are summarized in terms of the key components, such as rolling element bearings, gears and rotors. Finally, the outstanding open problems of EMD in fault diagnosis are discussed and potential future research directions are identified. It is expected that this review will serve as an introduction of EMD for those new to the concepts, as well as a summary of the current frontiers of its applications to fault diagnosis for experienced researchers.
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paper_title: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

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A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the empirical mode decomposition method with which any complicated data set can be dec...
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paper_title: Application of support vector regression machines to the processing of end effects of Hilbert-Huang transform

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Abstract The end effects of Hilbert–Huang transform are represented in two aspects. On the one hand, the end effects occur when the signal is decomposed by empirical mode decomposition (EMD) method. On the other hand, the end effects occur again while the Hilbert transforms are applied to the intrinsic mode functions (IMFs). To restrain the end effects of Hilbert–Huang transform, the support vector regression machines are used to predict the signals before the signal is decomposed by EMD method, thus the end effects could be restrained effectively and the IMFs with certain physical sense could be obtained. For the same purpose, the support vector regression machines are used again to predict the IMFs before the Hilbert transform of the IMFs, thus the accurate instantaneous frequencies and amplitudes could be obtained and the corresponding Hilbert spectrum with physical sense could be acquired. The analysis results from the simulation and experimental signals demonstrate that the end effects of Hilbert–Huang transform could be resolved effectively by the time series forecasting method based on support vector regression machines which is superior to that based on neural networks.
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paper_title: A confidence limit for the empirical mode decomposition and Hilbert spectral analysis

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The confidence limit is a standard measure of the accuracy of the result in any statistical analysis. Most of the confidence limits are derived as follows. The data are first divided into subsections and then, under the ergodic assumption, the temporal mean is substituted for the ensemble mean. Next, the confidence limit is defined as a range of standard deviations from this mean. However, such a confidence limit is valid only for linear and stationary processes. Furthermore, in order for the ergodic assumption to be valid, the subsections have to be statistically independent. For non‐stationary and nonlinear processes, such an analysis is no longer valid. The confidence limit of the method here termed EMD/HSA (for empirical mode decomposition/Hilbert spectral analysis) is introduced by using various adjustable stopping criteria in the sifting processes of the EMD step to generate a sample set of intrinsic mode functions (IMFs). The EMD technique acts as a pre‐processor for HSA on the original data, producing a set of components (IMFs) from the original data that equal the original data when added back together. Each IMF represents a scale in the data, from smallest to largest. The ensemble mean and standard deviation of the IMF sample sets obtained with different stopping criteria are calculated, and these form a simple random sample set. The confidence limit for EMD/HSA is then defined as a range of standard deviations from the ensemble mean. Without evoking the ergodic assumption, subdivision of the data stream into short sections is unnecessary; hence, the results and the confidence limit retain the full‐frequency resolution of the full dataset. This new confidence limit can be applied to the analysis of nonlinear and non‐stationary processes by these new techniques. Data from length‐of‐day measurements and a particularly violent recent earthquake are used to demonstrate how the confidence limit is obtained and applied. By providing a confidence limit for this new approach, a stable range of stopping criteria for the decomposition or sifting phase (EMD) has been established, making the results of the final processing with HSA, and the entire EMD/HSA method, more definitive.
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paper_title: ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD

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A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturall...
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paper_title: The local mean decomposition and its application to EEG perception data

paper_content:

This paper describes the local mean decomposition (LMD), a new iterative approach to demodulating amplitude and frequency modulated signals. The new method decomposes such signals into a set of functions, each of which is the product of an envelope signal and a frequency modulated signal from which a time-varying instantaneous frequency can be derived. The LMD method can be used to analyse a wide variety of natural signals such as electrocardiograms, functional magnetic resonance imaging data, and earthquake data. The paper presents the results of applying LMD to a set of scalp electroencephalogram (EEG) visual perception data. The LMD instantaneous frequency and energy structure of the EEG is examined, and compared with results obtained using the spectrogram. The nature of visual perception is investigated by measuring the degree of EEG instantaneous phase concentration that occurs following stimulus onset over multiple trials. The analysis suggests that there is a statistically significant difference between the theta phase concentrations of the perception and no perception EEG data.
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paper_title: ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD

paper_content:

A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturall...
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paper_title: Empirical Wavelet Transform

paper_content:

Some recent methods, like the empirical mode decomposition (EMD), propose to decompose a signal accordingly to its contained information. Even though its adaptability seems useful for many applications, the main issue with this approach is its lack of theory. This paper presents a new approach to build adaptive wavelets. The main idea is to extract the different modes of a signal by designing an appropriate wavelet filter bank. This construction leads us to a new wavelet transform, called the empirical wavelet transform. Many experiments are presented showing the usefulness of this method compared to the classic EMD.
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paper_title: Variational Mode Decomposition

paper_content:

During the late 1990s, Huang introduced the algorithm called Empirical Mode Decomposition, which is widely used today to recursively decompose a signal into different modes of unknown but separate spectral bands. EMD is known for limitations like sensitivity to noise and sampling. These limitations could only partially be addressed by more mathematical attempts to this decomposition problem, like synchrosqueezing, empirical wavelets or recursive variational decomposition. Here, we propose an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently. The model looks for an ensemble of modes and their respective center frequencies, such that the modes collectively reproduce the input signal, while each being smooth after demodulation into baseband. In Fourier domain, this corresponds to a narrow-band prior. We show important relations to Wiener filter denoising. Indeed, the proposed method is a generalization of the classic Wiener filter into multiple, adaptive bands. Our model provides a solution to the decomposition problem that is theoretically well founded and still easy to understand. The variational model is efficiently optimized using an alternating direction method of multipliers approach. Preliminary results show attractive performance with respect to existing mode decomposition models. In particular, our proposed model is much more robust to sampling and noise. Finally, we show promising practical decomposition results on a series of artificial and real data.
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paper_title: Wavelets and Time-Frequency Analysis

paper_content:

We present a selective overview of time-frequency analysis and some of its key problems. In particular we motivate the introduction of wavelet and wavelet packet analysis. Different types of decompositions of an idealized time-frequency plane provide the basis for understanding the performance of the numerical algorithms and their corresponding interpretations within the continuous models. As examples we show how to control the frequency spreading of wavelet packets at high frequencies using nonstationary filtering and study some properties of periodic wavelet packets. Furthermore we derive a formula to compute the time localization of a wavelet packet from its indexes which is exact for linear phase filters, and show how this estimate deteriorates with deviation from linear phase.
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paper_title: The wavelet transform, time-frequency localization and signal analysis

paper_content:

Two different procedures for effecting a frequency analysis of a time-dependent signal locally in time are studied. The first procedure is the short-time or windowed Fourier transform; the second is the wavelet transform, in which high-frequency components are studied with sharper time resolution than low-frequency components. The similarities and the differences between these two methods are discussed. For both schemes a detailed study is made of the reconstruction method and its stability as a function of the chosen time-frequency density. Finally, the notion of time-frequency localization is made precise, within this framework, by two localization theorems. >
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paper_title: Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples

paper_content:

Abstract Nonstationary signal analysis is one of the main topics in the field of machinery fault diagnosis. Time–frequency analysis can identify the signal frequency components, reveals their time variant features, and is an effective tool to extract machinery health information contained in nonstationary signals. Various time–frequency analysis methods have been proposed and applied to machinery fault diagnosis. These include linear and bilinear time–frequency representations (e.g., wavelet transform, Cohen and affine class distributions), adaptive parametric time–frequency analysis (based on atomic decomposition and time–frequency auto-regressive moving average models), adaptive non-parametric time–frequency analysis (e.g., Hilbert–Huang transform, local mean decomposition, and energy separation), and time varying higher order spectra. This paper presents a systematic review of over 20 major such methods reported in more than 100 representative articles published since 1990. Their fundamental principles, advantages and disadvantages, and applications to fault diagnosis of machinery have been examined. Some examples have also been provided to illustrate their performance.
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paper_title: Detection of Faults in Rotating Machinery Using Periodic Time-Frequency Sparsity

paper_content:

This paper addresses the problem of extracting periodic oscillatory features in vibration sig- nals for detecting faults in rotating machinery. To extract the feature, we propose an approach in the short-time Fourier transform (STFT) domain where the periodic oscillatory feature man- ifests itself as a relatively sparse grid. To estimate the sparse grid, we formulate an optimization problem using customized binary weights in the regularizer, where the weights are formulated to promote periodicity. In order to solve the proposed optimization problem, we develop an algorithm called augmented Lagrangian majorization-minimization algorithm, which combines the split augmented Lagrangian shrinkage algorithm (SALSA) with majorization-minimization (MM), and is guaranteed to converge for both convex and non-convex formulation. As examples, the proposed approach is applied to simulated data, and used as a tool for diagnosing faults in bearings and gearboxes for real data, and compared to some state-of-the-art methods. The results show the proposed approach can effectively detect and extract the periodical oscillatory features.
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paper_title: A windowing and mapping strategy for gear tooth fault detection of a planetary gearbox

paper_content:

Abstract When there is a single cracked tooth in a planet gear, the cracked tooth is enmeshed for very short time duration in comparison to the total time of a full revolution of the planet gear. The fault symptom generated by the single cracked tooth may be very weak. This study aims to develop a windowing and mapping strategy to interpret the vibration signal of a planetary gear at the tooth level. The fault symptoms generated by a single cracked tooth of the planet gear of interest can be extracted. The health condition of the planet gear can be assessed by comparing the differences among the signals of all teeth of the planet gear. The proposed windowing and mapping strategy is tested with both simulated vibration signals and experimental vibration signals. The tooth signals can be successfully decomposed and a single tooth fault on a planet gear can be effectively detected.
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paper_title: Fault feature extraction of rolling element bearings using sparse representation

paper_content:

Abstract Influenced by factors such as speed fluctuation, rolling element sliding and periodical variation of load distribution and impact force on the measuring direction of sensor, the impulse response signals caused by defective rolling bearing are non-stationary, and the amplitudes of the impulse may even drop to zero when the fault is out of load zone. The non-stationary characteristic and impulse missing phenomenon reduce the effectiveness of the commonly used demodulation method on rolling element bearing fault diagnosis. Based on sparse representation theories, a new approach for fault diagnosis of rolling element bearing is proposed. The over-complete dictionary is constructed by the unit impulse response function of damped second-order system, whose natural frequencies and relative damping ratios are directly identified from the fault signal by correlation filtering method. It leads to a high similarity between atoms and defect induced impulse, and also a sharply reduction of the redundancy of the dictionary. To improve the matching accuracy and calculation speed of sparse coefficient solving, the fault signal is divided into segments and the matching pursuit algorithm is carried out by segments. After splicing together all the reconstructed signals, the fault feature is extracted successfully. The simulation and experimental results show that the proposed method is effective for the fault diagnosis of rolling element bearing in large rolling element sliding and low signal to noise ratio circumstances.
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paper_title: Transients Extraction Based on Averaged Random Orthogonal Matching Pursuit Algorithm for Machinery Fault Diagnosis

paper_content:

Sparse representation based on matching pursuit (MP) algorithm is one of the effective methods for the extraction of weak feature contaminated by heavy noise. However, the optimal iterative threshold and iterations of MP algorithm are difficult to be determined and the sparsest representation is difficult to obtain for pursuit algorithms. In order to reduce the influence of the threshold and iterations on the performance of MP and obtain appropriate results without seeking the sparsest representation, a new transient feature extraction technique named averaged random orthogonal MP (AROMP) algorithm is proposed. In the proposed method, random orthogonal MP algorithm, which is a greedy algorithm to match the atoms with probability, is utilized repeatedly to generate a group of competitive representations for the mechanical vibration signal. Then by averaging these solutions, the estimated representation vector can be obtained to represent the vibration signal and then transients can be extracted from the noisy signal. The simulation study and experimental analysis show that transients can be extracted effectively from the noisy vibration signal. And comparison results between the proposed method and orthogonal MP show that the proposed algorithm is less dependent on iterations and can obtain a better performance for transients extraction. Comparisons between the proposed algorithm and spectral kurtosis also show the superiority of the proposed method.
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paper_title: Sparse Feature Identification Based on Union of Redundant Dictionary for Wind Turbine Gearbox Fault Diagnosis

paper_content:

A primary challenge in fault diagnosis is to extract multiple components entangled within a noisy observation. Therefore, this paper describes and analyzes a novel framework, based on convex optimization, for simultaneously identifying multiple features from superimposed signals. This work adequately exploits the underlying prior information that multiple faults with similar frequency spectrum have different morphological waveforms that can be sparsely represented over the union of redundant dictionaries. Within this framework, prior information is formulated into regularization terms, and a sparse optimization problem, which can be solved through the alternating direction method of multipliers (ADMM), is proposed. Meanwhile, the convergence and computational complexity of the proposed iterative framework are profoundly investigated. Moreover, sensitivity analyses and adaptive selection rules for the regularization parameters are described in detail through a set of comprehensive numerical studies. The proposed framework is validated through performing the diagnosis of multiple faults for gearbox in a wind farm. The comparison with respect to the state of the art in the field is illustrated in detail, which highlights the superiority of the proposed framework.
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paper_title: Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: A review with examples

paper_content:

Abstract Complex signal analysis is a key topic in machinery fault diagnosis. For complex multi-component signals of various morphological contents, the commonly used basis expansion based signal processing method lacks adaptability and flexibility, thus being ineffective to extract the embedded meaningful information. Sparse signal representation has excellent adaptability and high flexibility in describing arbitrary complex signals based on atomic decomposition over redundant and over-complete dictionary, thus being free from the limitations imposed by orthogonal basis, and providing an effective approach to feature extraction from intricate signals for machinery fault diagnosis. This paper presents a systematic review on sparse signal representation, especially on two key topics, i.e. atomic decomposition algorithms (such as greedy pursuit, l p norm regularization and iterative shrinkage/thresholding) and dictionary design methods (including analytic dictionary design and dictionary learning) reported in more than 70 representative articles published since 1990. Their fundamental principles, advantages and disadvantages, and applications to machinery fault diagnosis, are examined. Some examples are provided to illustrate their performance.
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paper_title: Quantitative fault analysis of roller bearings based on a novel matching pursuit method with a new step-impulse dictionary

paper_content:

Abstract A novel matching pursuit method based on a new step-impulse dictionary to measure the size of a bearing׳s spall-like fault is presented in this study. Based on the seemingly double-impact theory and the rolling bearing fault mechanism, a theoretical model for the bearing fault with different spall-like fault sizes is developed and analyzed, and the seemingly double-impact characteristic of the bearing faults is explained. The first action that causes a bearing fault is due to the entry of the roller element into the spall-like fault which can be described as a step-like response. The second action is the exit of the roller element from the spall-like fault, which can be described as an impulse-like response. Based on the quantitative relationship between the time interval of the seemingly double-impact actions and the fault size, a novel matching pursuit method is proposed based on a new step-impulse dictionary. In addition, the quantitative matching pursuit algorithm is proposed for bearing fault diagnosis based on the new dictionary model. Finally, an atomic selection mechanism is proposed to improve the measurement accuracy of bearing fault size. The simulation results of this study indicate that the new matching pursuit method based on the new step-impulse dictionary can be reliably used to measure the sizes of bearing spall-like faults. The applications of this method to the fault signals of bearing outer-races measured at different speeds have shown that the proposed method can effectively measure a bearing׳s spall-like fault size.
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paper_title: Artificial intelligence for fault diagnosis of rotating machinery: A review

paper_content:

Abstract Fault diagnosis of rotating machinery plays a significant role for the reliability and safety of modern industrial systems. As an emerging field in industrial applications and an effective solution for fault recognition, artificial intelligence (AI) techniques have been receiving increasing attention from academia and industry. However, great challenges are met by the AI methods under the different real operating conditions. This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications. A brief introduction of different AI algorithms is presented first, including the following methods: k -nearest neighbour, naive Bayes, support vector machine, artificial neural network and deep learning. Then, a broad literature survey of these AI algorithms in industrial applications is given. Finally, the advantages, limitations, practical implications of different AI algorithms, as well as some new research trends, are discussed.
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paper_title: Rolling element bearings diagnostics using the Symbolic Aggregate approXimation

paper_content:

Abstract Rolling element bearings are a very critical component in various engineering assets. Therefore it is of paramount importance the detection of possible faults, especially at an early stage, that may lead to unexpected interruptions of the production or worse, to severe accidents. This research work introduces a novel, in the field of bearing fault detection, method for the extraction of diagnostic representations of vibration recordings using the Symbolic Aggregate approXimation (SAX) framework and the related intelligent icons representation. SAX essentially transforms the original real valued time-series into a discrete one, which is then represented by a simple histogram form summarizing the occurrence of the chosen symbols/words. Vibration signals from healthy bearings and bearings with three different fault locations and with three different severity levels, as well as loading conditions, are analyzed. Considering the diagnostic problem as a classification one, the analyzed vibration signals and the resulting feature vectors feed simple classifiers achieving remarkably high classification accuracies. Moreover a sliding window scheme combined with a simple majority voting filter further increases the reliability and robustness of the diagnostic method. The results encourage the potential use of the proposed methodology for the diagnosis of bearing faults.
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paper_title: Neighborhood size selection in the k-nearest-neighbor rule using statistical confidence

paper_content:

The k-nearest-neighbor rule is one of the most attractive pattern classification algorithms. In practice, the choice of k is determined by the cross-validation method. In this work, we propose a new method for neighborhood size selection that is based on the concept of statistical confidence. We define the confidence associated with a decision that is made by the majority rule from a finite number of observations and use it as a criterion to determine the number of nearest neighbors needed. The new algorithm is tested on several real-world datasets and yields results comparable to the k-nearest-neighbor rule. However, in contrast to the k-nearest-neighbor rule that uses a fixed number of nearest neighbors throughout the feature space, our method locally adjusts the number of nearest neighbors until a satisfactory level of confidence is reached. In addition, the statistical confidence provides a natural way to balance the trade-off between the reject rate and the error rate by excluding patterns that have low confidence levels. We believe that this property of our method can be of great importance in applications where the confidence with which a decision is made is equally or more important than the overall error rate.
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paper_title: Nearest Neighbor Pattern Classification

paper_content:

The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points. This rule is independent of the underlying joint distribution on the sample points and their classifications, and hence the probability of error R of such a rule must be at least as great as the Bayes probability of error R^{\ast} --the minimum probability of error over all decision rules taking underlying probability structure into account. However, in a large sample analysis, we will show in the M -category case that R^{\ast} \leq R \leq R^{\ast}(2 --MR^{\ast}/(M-1)) , where these bounds are the tightest possible, for all suitably smooth underlying distributions. Thus for any number of categories, the probability of error of the nearest neighbor rule is bounded above by twice the Bayes probability of error. In this sense, it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
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paper_title: Diagnostics of gear faults using ensemble empirical mode decomposition, hybrid binary bat algorithm and machine learning algorithms

paper_content:

Early fault detection is a challenge in gear fault diagnosis. In particular, efficient feature extraction and feature selection is a key issue to automatic condition monitoring and fault diagnosis processes. In order to focus on those issues, this paper presents a study that uses ensemble empirical mode decomposition (EEMD) to extract features and hybrid binary bat algorithm (HBBA) hybridized with machine learning algorithm to reduce the dimensionality as well to select the predominant features which contains the necessary discriminative information. Efficiency of the approaches are evaluated using standard classification metrics such as Nearest neighbours, C4.5, DTNB, K star and JRip. The gear fault experiments were conducted, acquired the vibration signals for different gear states such as normal, frosting, pitting and crack, under constant motor speed and constant load. The proposed method is applied to identify the different gear faults at early stage and the results demonstrate its effectiveness.
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paper_title: Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis

paper_content:

Vibration and acoustic analysis actively support the nondestructive and noninvasive fault diagnostics of rotating machines at early stages. Nonetheless, the acoustic signal is less used because of its vulnerability to external interferences, hindering an efficient and robust analysis for condition monitoring (CM). This paper presents a novel methodology to characterize different failure signatures from rotating machines using either acoustic or vibration signals. Firstly, the signal is decomposed into several narrow-band spectral components applying different filter bank methods such as empirical mode decomposition, wavelet packet transform, and Fourier-based filtering. Secondly, a feature set is built using a proposed similarity measure termed cumulative spectral density index and used to estimate the mutual statistical dependence between each bandwidth-limited component and the raw signal. Finally, a classification scheme is carried out to distinguish the different types of faults. The methodology is tested in two laboratory experiments, including turbine blade degradation and rolling element bearing faults. The robustness of our approach is validated contaminating the signal with several levels of additive white Gaussian noise, obtaining high-performance outcomes that make the usage of vibration, acoustic, and vibroacoustic measurements in different applications comparable. As a result, the proposed fault detection based on filter bank similarity features is a promising methodology to implement in CM of rotating machinery, even using measurements with low signal-to-noise ratio.
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paper_title: Fault detection and diagnosis in synchronous motors using hidden Markov model-based semi-nonparametric approach

paper_content:

Early detection and diagnosis of faults in industrial machines would reduce the maintenance cost and also increase the overall equipment effectiveness by increasing the availability of the machinery systems. In this paper, a semi-nonparametric approach based on hidden Markov model is introduced for fault detection and diagnosis in synchronous motors. In this approach, after training the hidden Markov model classifiers (parametric stage), two matrices named probabilistic transition frequency profile and average probabilistic emission are computed based on the hidden Markov models for each signature (nonparametric stage) using probabilistic inference. These matrices are later used in forming a similarity scoring function, which is the basis of the classification in this approach. Moreover, a preprocessing method, named squeezing and stretching is proposed which rectifies the difficulty of dealing with various operating speeds in the classification process. Finally, the experimental results are provided and compared. Further investigations are carried out, providing sensitivity analysis on the length of signatures, the number of hidden state values, as well as statistical performance evaluation and comparison with conventional hidden Markov model-based fault diagnosis approach. Results indicate that implementation of the proposed preprocessing, which unifies the signatures from various operating speeds, increases the classification accuracy by nearly 21% and moreover utilization of the proposed semi-nonparametric approach improves the accuracy further by nearly 6%.
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paper_title: A novel fault diagnosis method for rotating machinery based on S transform and morphological pattern spectrum

paper_content:

With the continuing expansion of the applications of rotating machinery, an earlier and more accurate fault diagnosis method is required. In this paper, a novel characterization method based on S transform and morphological pattern spectrum (ST-MPS) was put forward. In order to verify the application of the method, ST-MPS was applied to a set of experimental signals obtained in a bearing test bench, and the results verified that the proposed feature extraction method is an effective approach to accurately classify the types of bearing fault.
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paper_title: Single-layer learning revisited: a stepwise procedure for building and training a neural network

paper_content:

A stepwise procedure for building and training a neural network intended to perform classification tasks, based on single layer learning rules, is presented. This procedure breaks up the classification task into subtasks of increasing complexity in order to make learning easier. The network structure is not fixed in advance: it is subject to a growth process during learning. Therefore, after training, the architecture of the network is guaranteed to be well adapted for the classification problem.
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paper_title: Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine

paper_content:

Roller bearings are widely used in rotating machinery and one of the major reasons for machine breakdown is their failure. Vibration based condition monitoring is the most common method for extracting some important information to identify bearing defects. However, acquired acceleration signals are usually noisy, which significantly affects the results of fault diagnosis. Wavelet packet decomposition (WPD) is a powerful method utilized effectively for the denoising of the signals acquired. Furthermore, Ensemble empirical mode decomposition (EEMD) is a newly developed decomposition method to solve the mode mixing problem of empirical mode decomposition (EMD), which is a consequence of signal intermittence. In this study a combined automatic method is proposed to detect very small defects on roller bearings. WPD is applied to clean the noisy signals acquired, then informative feature vectors are extracted using the EEMD technique. Finally, the states of the bearings are examined by labeling the samples using the hyperplane constructed by the support vector machine algorithm. The data were generated by means of a test rig assembled in the labs of the Dynamics and Identification Research Group in the mechanical and aerospace engineering department, Politecnico di Torino. Various operating conditions (three shaft speeds, three external loads and a very small damage size on a roller) were considered to obtain reliable results. It is shown that the combined method proposed is able to identify the states of the bearings effectively.
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paper_title: A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

paper_content:

Feature extraction is one of the most important, pivotal, and difficult problems in mechanical fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of early fault prediction. Therefore, a new fault feature extraction method, called the EDOMFE method based on integrating ensemble empirical mode decomposition (EEMD), mode selection, and multi-scale fuzzy entropy is proposed to accurately diagnose fault in this paper. The EEMD method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs) with a different physical significance. The correlation coefficient analysis method is used to calculate and determine three improved IMFs, which are close to the original signal. The multi-scale fuzzy entropy with the ability of effective distinguishing the complexity of different signals is used to calculate the entropy values of the selected three IMFs in order to form a feature vector with the complexity measure, which is regarded as the inputs of the support vector machine (SVM) model for training and constructing a SVM classifier (EOMSMFD based on EDOMFE and SVM) for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by real bearing vibration signals of the motor with different loads and fault severities. The experiment results show that the proposed EDOMFE method can effectively extract fault features from the vibration signal and that the proposed EOMSMFD method can accurately diagnose the fault types and fault severities for the inner race fault, the outer race fault, and rolling element fault of the motor bearing. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery.
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paper_title: Singular value decomposition based feature extraction approaches for classifying faults of induction motors

paper_content:

Abstract This paper proposes singular value decomposition (SVD)-based feature extraction methods for fault classification of an induction motor: a short-time energy (STE) plus SVD technique in the time-domain analysis, and a discrete cosine transform (DCT) plus SVD technique in the frequency-domain analysis. To early identify induction motor faults, the extracted features are utilized as the inputs of multi-layer support vector machines (MLSVMs). Since SVMs perform well with the radial basis function (RBF) kernel for appropriately categorizing the faults of the induction motor, it is important to explore the impact of the σ values for the RBF kernel, which affects the classification accuracy. Likewise, this paper quantitatively evaluates the classification accuracy with different numbers of features, because the number of features affects the classification accuracy. According to the experimental results, although SVD-based features are effective for a noiseless environment, the STE plus SVD feature extraction approach is more effective with and without sensor noise in terms of the classification accuracy than the DCT plus SVD feature extraction approach. To demonstrate the improved classification of the proposed approach for identifying faults of the induction motor, the proposed SVD based feature extraction approach is compared with other state-of-the art methods and yields higher classification accuracies for both noiseless and noisy environments than conventional approaches.
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paper_title: Large margin dags for multiclass classification

paper_content:

We present a new learning architecture: the Decision Directed Acyclic Graph (DDAG), which is used to combine many two-class classifiers into a multiclass classifier. For an N-class problem, the DDAG contains N(N - 1)/2 classifiers, one for each pair of classes. We present a VC analysis of the case when the node classifiers are hyperplanes; the resulting bound on the test error depends on N and on the margin achieved at the nodes, but not on the dimension of the space. This motivates an algorithm, DAGSVM, which operates in a kernel-induced feature space and uses two-class maximal margin hyperplanes at each decision-node of the DDAG. The DAGSVM is substantially faster to train and evaluate than either the standard algorithm or Max Wins, while maintaining comparable accuracy to both of these algorithms.
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paper_title: Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis

paper_content:

Abstract Bearing plays an essential role in the performance of mechanical system and fault diagnosis of mechanical system is inseparably related to the diagnosis of the bearings. However, it is a challenge to detect weak fault from the complex and non-stationary vibration signals with a large amount of noise, especially at the early stage. To improve the anti-noise ability and detect incipient fault, a novel fault detection method based on a short-time matching method and Support Vector Machine (SVM) is proposed. In this paper, the mechanism of roller bearing is discussed and the impact time frequency dictionary is constructed targeting the multi-component characteristics and fault feature of roller bearing fault vibration signals. Then, a short-time matching method is described and the simulation results show the excellent feature extraction effects in extremely low signal-to-noise ratio (SNR). After extracting the most relevance atoms as features, SVM was trained for fault recognition. Finally, the practical bearing experiments indicate that the proposed method is more effective and efficient than the traditional methods in weak impact signal oscillatory characters extraction and incipient fault diagnosis.
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paper_title: A novel approach to wavelet selection and tree kernel construction for diagnosis of rolling element bearing fault

paper_content:

A novel methodology for early diagnosis of rolling element bearing fault is employed based on continuous wavelet transform (CWT) and support vector machine (SVM). CWT is especially suited for analyzing non-stationary signals in time–frequency domain where time information is retained as well as frequency content. To better approximate non-stationary vibration signals from rolling element bearing, a wavelet choice criterion is established to select an appropriate mother wavelet for feature extraction. The Shannon wavelet is picked out of several considered wavelets. The classification tree kernels (CTK) are constructed to address nonlinear classification of the characteristic samples derived from the wavelet coefficients. By using Fuzzy pruning strategy, a large variety of classification trees are generated. The trees with diverse structures can effectively explore intrinsic information among samples. Then, the tree kernel matrices can be acquired through ensemble statistical learning, which eventually reveal the similarity of samples objectively and stably. Under such architecture of kernel methods, a classification tree kernel based support vector machine (CTKSVM) is proposed to identify bearing fault. The performance of the methodology involving CWT and CTKSVM (CWT–CTKSVM) is evaluated by cross validation and independent test. The results show that the CWT–CTKSVM totally is superior to other SVM methods with common kernels. Therefore, it is a prospective technique for detection and identification of rolling element bearing fault.
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paper_title: Knowledge extraction using data mining for multi-class fault diagnosis of induction motor

paper_content:

The feature extraction capability of rough set theory (RST) and genetic algorithm (GA) are used to extract knowledge from radial frame vibration signal for fault diagnosis of induction motor. This knowledge can assist in selecting scales for continuous wavelet transform (CWT) and mono-components required for Hilbert transform (HT) to extract fault related information from the vibration signal. Thus, the computational complexity of the signal processing tools is considerably reduced making both CWT and HT hardware friendly and suitable for real-time applications. For machine learning based automatic multi-class fault diagnosis, the performance of the classifiers are also considerably improved with significant reduction in computational burden since the redundant and irrelevant information can be effectively removed. The information obtained using data mining technique is successfully used to detect six types of induction motor faults. The results obtained are also verified in presence of high level of noise which has not been attempted earlier. The main contribution of the paper is to combine the advantages of two powerful signal processing tool like CWT and HT to extract hidden information from vibration signal in conjunction with data mining technique making them computationally efficient and easy to implement. A novel but simple approach to remove redundant and irrelevant information, to extract the important knowledge (fault features) using data mining.Knowledge generated using CWT are redundant whereas that from HT are mostly irrelevant. Useful knowledge extracted using RST and GA.Performances of the data mining techniques have been judged by taking into account their applicability in two different types of dataset.Only radial (vertical) frame vibration is found sufficient for multi-class fault diagnosis of induction motor.
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paper_title: Quantitative and Localization Diagnosis of a Defective Ball Bearing Based on Vertical–Horizontal Synchronization Signal Analysis

paper_content:

A novel method is proposed for quantitative and localization diagnosis of a bearing outer ring defect in this paper. This method aims to distinguish these defects with different angular positions and different sizes based on the natural multiples of the ball angular spacing. Defects that differ in size by natural multiples of the ball angular spacing will have the same time interval, which may lead to misdiagnosis. Distinguishing such defects accurately is the goal for some industries. Therefore, the synchronized signal in the vertical and horizontal directions of the bearing systems with different angular positions and different size defects are quantitatively analyzed based on the presented five indices to explore the mechanism and method of distinguishing these defects. The simulation and experimental results show that the vertical–horizontal synchronized root mean square (VHSRMS) index can distinguish such defects effectively, and the defect angle position can be diagnosed using the VHSRMS–1 . More importantly, the VHS analysis method expands the location of the outer ring defect to 265–275° in quantitative diagnosis and realizes the localization diagnosis of the defect angle position. The research results provide a new approach and method for quantitative and localization diagnosis of a defective bearing.
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paper_title: Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR

paper_content:

Abstract A significant number of failures of wind turbine drivetrains occur in the high-speed shaft bearings. In this paper, a vibration-based prognostic and health monitoring methodology for wind turbine high-speed shaft bearing (HSSB) is proposed using a spectral kurtosis (SK) data-driven approach. Indeed, time domain indices derived from SK are used and a comparative study is performed with frequently used time-domain features in the bearing degradation health assessment. The effectiveness is quantified by two measures, i.e., monotonicity and trendability. Among those features, the area under SK is utilized for the first time as a condition indicator of rolling bearing fault. A support vector regression (SVR) model was trained and tested for the prediction of the HSSB lifetime prognostics, showing the superiority of SK-derived indices of degradation assessment. We verified the potential of the prognostics method using real measured data from a drivetrain wind turbine. The experimental results show that the proposed approach can successfully detect an early failure and can better estimate the degradation trend of HSSB than traditional time-domain vibration features.
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paper_title: Recognition of rolling bearing fault patterns and sizes based on two-layer support vector regression machines

paper_content:

The fault diagnosis of rolling element bearings has drawn considerable research attention in recent years because these fundamental elements frequently suffer failures that could result in unexpected machine breakdowns. Artificial intelligence algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) have been widely investigated to identify various faults. However, as the useful life of a bearing deteriorates, identifying early bearing faults and evaluating their sizes of development are necessary for timely maintenance actions to prevent accidents. This study proposes a new two-layer structure consisting of support vector regression machines (SVRMs) to recognize bearing fault patterns and track the fault sizes. The statistical parameters used to track the fault evolutions are first extracted to condense original vibration signals into a few compact features. The extracted features are then used to train the proposed two-layer SVRMs structure. Once these parameters of the proposed two-layer SVRMs structure are determined, the features extracted from other vibration signals can be used to predict the unknown bearing health conditions. The effectiveness of the proposed method is validated by experimental datasets collected from a test rig. The results demonstrate that the proposed method is highly accurate in differentiating between fault patterns and determining their fault severities. Further, comparisons are performed to show that the proposed method is better than some existing methods.
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paper_title: A hierarchical method for multi-class support vector machines

paper_content:

We introduce a framework, which we call Divide-by-2 (DB2), for extending support vector machines (SVM) to multi-class problems. DB2 offers an alternative to the standard one-against-one and one-against-rest algorithms. For an N class problem, DB2 produces an N − 1 node binary decision tree where nodes represent decision boundaries formed by N − 1 SVM binary classifiers. This tree structure allows us to present a generalization and a time complexity analysis of DB2. Our analysis and related experiments show that, DB2 is faster than one-against-one and one-against-rest algorithms in terms of testing time, significantly faster than one-against-rest in terms of training time, and that the cross-validation accuracy of DB2 is comparable to these two methods.
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paper_title: Automatic bearing fault diagnosis based on one-class ν-SVM

paper_content:

Rolling-element bearings are among the most used elements in industrial machinery, thus an early detection of a defect in these components is necessary to avoid major machine failures. Vibration analysis is a widely used condition monitoring technique for high-speed rotating machinery. Using the information contained in the vibration signals, an automatic method for bearing fault detection and diagnosis is presented in this work. Initially, a one-class @n-SVM is used to discriminate between normal and faulty conditions. In order to build a model of normal operation regime, only data extracted under normal conditions is used. Band-pass filters and Hilbert Transform are then used sequentially to obtain the envelope spectrum of the original raw signal that will finally be used to identify the location of the problem. In order to check the performance of the method, two different data sets are used: (a) real data from a laboratory test-to-failure experiment and (b) data obtained from a fault-seeded bearing test. The results showed that the method was able not only to detect the failure in an incipient stage but also to identify the location of the defect and qualitatively assess its evolution over time.
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paper_title: Logistic regression and artificial neural network classification models: a methodology review

paper_content:

Logistic regression and artificial neural networks are the models of choice in many medical data classification tasks. In this review, we summarize the differences and similarities of these models from a technical point of view, and compare them with other machine learning algorithms. We provide considerations useful for critically assessing the quality of the models and the results based on these models. Finally, we summarize our findings on how quality criteria for logistic regression and artificial neural network models are met in a sample of papers from the medical literature.
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paper_title: Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing

paper_content:

Abstract Rolling bearings are widely used in rotary machinery systems. The measured vibration signal of any part linked to rolling bearings contains fault information when failure occurs, differing only by energy levels. Bearing failure will cause the vibration of other components, and therefore the collected bearing vibration signals are mixed with vibration signal of other parts and noise. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can avoid the loss of local information. Subsequently using the multivariate empirical mode decomposition (multivariate EMD) to simultaneously analyze the multivariate signal is beneficial to extract fault information, especially for weak fault characteristics during the period of early failure. This paper proposes a novel method for fault feature extraction of rolling bearing based on multivariate EMD. The nonlocal means (NL-means) denoising method is used to preprocess the multivariate signal and the correlation analysis is employed to calculate fault correlation factors to select effective intrinsic mode functions (IMFs). Finally characteristic frequencies are extracted from the selected IMFs by spectrum analysis. The numerical simulations and applications to bearing monitoring verify the effectiveness of the proposed method and indicate that this novel method is promising in the field of signal decomposition and fault diagnosis.
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paper_title: Variable cosine windowing of intrinsic mode functions: Application to gear fault diagnosis

paper_content:

Gear pair is used for speed reduction or increasing torque and/or to change the direction of rotation. Gears are considered critical element in various mechanical systems. When gears are in use, the multi component vibration signals are generated. These vibration signals can be captured by mounting accelerometers at suitable locations. Vibration signal analysis is very effective tool in finding gear fault at early stage. The methods based on empirical mode decomposition (EMD) have been used for gear fault diagnosis in mechanical systems. The EMD method decomposes an original signal into different frequency-bands in time domain, known as intrinsic mode functions (IMFs). A serious problem in application of EMD is boundary distortion of IMFs. While doing statistical analysis of IMFs, boundary distortion may provide high values of statistical indicator (e.g. kurtosis, Sr, Sα), even if fault is not present. Several extension-based methods are employed to eliminate the boundary distortion problem. Extension-based methods cannot completely eliminate the boundary distortion, especially when the low-frequency component of the analyzed signal is weak. Recently, cosine window-based method has been proposed by which the boundary distortion can be controlled in boundaries of the signal and the middle component of it can be exactly decomposed into IMFs. The cosine window-based method works only for a particular IMF depending on the size of window. Since, in EMD process, the boundary distortion of successive IMFs increases, a variable cosine window is proposed in this paper to address the increasing boundary distortion problem. In the proposed method boundary distortion problem is minimized by using variable cosine window for all IMFs. The simulation and experimental results for three statistical indicators viz. kurtosis, Sr, Sα show that the proposed method based on variable cosine window is a powerful and reliable technique for fault diagnosis.
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paper_title: Quantitative diagnosis of a spall-like fault of a rolling element bearing by empirical mode decomposition and the approximate entropy method

paper_content:

Abstract Spalling or pitting is the main manifestation of fault development in a bearing during the earlier stages. Previous studies indicated that the vibration signal of a bearing with a spall-like defect may be composed of two parts; the first part originates from the entry of the rolling element into the spall-like area, and the second part refers to the exit from the fault region. The quantitative diagnosis of a spall-like fault of the rolling element bearing can be realised if the entry–exit event times can be accurately calculated. However, the vibration signal of a faulty bearing is usually non-stationary and non-linear with strong background noise interference. Meanwhile, the signal energy from the early spall region is too low to accurately register the features of the entry–exit event in the time domain. In this work, the approximate entropy (ApEn) method and empirical mode decomposition (EMD) are applied to clearly separate the entry–exit events, and thus the size of the spall-like fault is estimated. First, the original acceleration vibration signal is decomposed by EMD, and some useful intrinsic mode function (IMF) components are obtained. Second, the concept of IMF-ApEn is introduced, which can directly reflect the complexity of the IMFs using the actual vibration signal. The IMF-ApEn distributions of different noise signals illustrate that the process of complexity changes when a full spectrum process is split into its IMFs. Third, a unit white noise IMF-ApEn distribution template serves as a sieve to extract the ( effective intrinsic mode functions ) EIMF components, and thus the entry and exit events in the response signal are distinguished. The IMF-ApEn method is further compared with a previous method (N. Sawalhi's method) to test its superiority. The dynamic effects are investigated when the ball element enters a spall-like region by computer simulation. The simulation and the experimental results show that the approach to the quantitative diagnosis of a rolling element bearing based on IMF-ApEn has higher veracity and good robustness.
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paper_title: Rolling bearing diagnosing method based on Empirical Mode Decomposition of machine vibration signal

paper_content:

Abstract Rolling bearing faults are one of the major reasons for breakdown of industrial machinery and bearing diagnosing is one of the most important topics in machine condition monitoring. The main problem in industrial application of bearing vibration diagnostics is the masking of informative bearing signal by machine noise. The vibration signal of the rolling bearing is often covered or concealed by other structural vibrations sources, such as gears. Although a number of vibration diagnostic techniques have been developed over the last several years, in many cases these methods are quite complicated in use or only effective at later stages of damage development. This paper presents an EMD -based rolling bearing diagnosing method that shows potential for bearing damage detection at a much earlier stage of damage development. By using EMD a raw vibration signal is decomposed into a number of Intrinsic Mode Functions ( IMF s). Then, a new method of IMF s aggregation into three Combined Mode Functions ( CMF s) is applied and finally the vibration signal is divided into three parts of signal: noise-only part, signal-only part and trend-only part. To further bearing fault-related feature extraction from resultant signals, the spectral analysis of the empirically determined local amplitude is used. To validate the proposed method, raw vibration signals generated by complex mechanical systems employed in the industry (driving units of belt conveyors), including normal and fault bearing vibration data, are used in two case studies. The results show that the proposed rolling bearing diagnosing method can identify bearing faults at early stages of their development.
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paper_title: Incipient fault diagnosis of roller bearings using empirical mode decomposition and correlation coefficient

paper_content:

In this paper, an incipient fault diagnosis method for roller bearings is proposed, based on empirical mode decomposition (EMD) and correlation coefficient (the normalized value of the cross-correlation function at the zero-lag point). The high frequency resonance phenomena begin to emerge as a defect forms gradually in the bearings. Therefore the high frequency content is sensitive to the change of the bearing conditions. Based on this, the EMD method is firstly applied to the bearing vibration signals to obtain some intrinsic mode functions (IMF) which contain different frequency bands from high to low. The first IMF of the signal to be detected, representing the high frequency band, is then selected to calculate the correlation coefficient between its frequency-domain signal and that of normal state. The correlation coefficient can demonstrate the fault evolution process and thus can detect an early fault. Finally the early faulty signals are analyzed by using the envelope analysis and the location of the fault is identified. The experimental results verify the effectiveness of the proposed method.
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paper_title: Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings

paper_content:

This paper presents a novel signal processing scheme, bandwidth empirical mode decomposition, and adaptive multiscale morphological analysis (BEMD-AMMA) for early fault diagnosis of rolling bearings. In this scheme, we propose a bandwidth based method to select the best envelope interpolation method. First, multiple envelope algorithms are defined and separately subtracted from the original data to obtain the preintrinsic mode functions (PIMFs). Second, an IMF with the smallest frequency bandwidth is selected to be the optimal IMF (OIMF). Third, this OIMF is subtracted from the original signal, and then repeat the sifting process until the residual is a constant or monotonic. Since the OIMF has the smallest frequency bandwidth, the mode mixing phenomenon can be significantly weakened. After that the OIMFs with clear fault information are used to construct the main component of the original signal. Then, the AMMA is introduced to demodulate the constructed main component. Simulation and experimental vibration signals are employed to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms EMD-AMMA, ensemble empirical mode decomposition-AMMA, and generalized empirical mode decomposition-empirical envelope demodulation in detecting early inner race fault.
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paper_title: Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing

paper_content:

Abstract Rolling bearings are widely used in rotary machinery systems. The measured vibration signal of any part linked to rolling bearings contains fault information when failure occurs, differing only by energy levels. Bearing failure will cause the vibration of other components, and therefore the collected bearing vibration signals are mixed with vibration signal of other parts and noise. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can avoid the loss of local information. Subsequently using the multivariate empirical mode decomposition (multivariate EMD) to simultaneously analyze the multivariate signal is beneficial to extract fault information, especially for weak fault characteristics during the period of early failure. This paper proposes a novel method for fault feature extraction of rolling bearing based on multivariate EMD. The nonlocal means (NL-means) denoising method is used to preprocess the multivariate signal and the correlation analysis is employed to calculate fault correlation factors to select effective intrinsic mode functions (IMFs). Finally characteristic frequencies are extracted from the selected IMFs by spectrum analysis. The numerical simulations and applications to bearing monitoring verify the effectiveness of the proposed method and indicate that this novel method is promising in the field of signal decomposition and fault diagnosis.
---
paper_title: Variable cosine windowing of intrinsic mode functions: Application to gear fault diagnosis

paper_content:

Gear pair is used for speed reduction or increasing torque and/or to change the direction of rotation. Gears are considered critical element in various mechanical systems. When gears are in use, the multi component vibration signals are generated. These vibration signals can be captured by mounting accelerometers at suitable locations. Vibration signal analysis is very effective tool in finding gear fault at early stage. The methods based on empirical mode decomposition (EMD) have been used for gear fault diagnosis in mechanical systems. The EMD method decomposes an original signal into different frequency-bands in time domain, known as intrinsic mode functions (IMFs). A serious problem in application of EMD is boundary distortion of IMFs. While doing statistical analysis of IMFs, boundary distortion may provide high values of statistical indicator (e.g. kurtosis, Sr, Sα), even if fault is not present. Several extension-based methods are employed to eliminate the boundary distortion problem. Extension-based methods cannot completely eliminate the boundary distortion, especially when the low-frequency component of the analyzed signal is weak. Recently, cosine window-based method has been proposed by which the boundary distortion can be controlled in boundaries of the signal and the middle component of it can be exactly decomposed into IMFs. The cosine window-based method works only for a particular IMF depending on the size of window. Since, in EMD process, the boundary distortion of successive IMFs increases, a variable cosine window is proposed in this paper to address the increasing boundary distortion problem. In the proposed method boundary distortion problem is minimized by using variable cosine window for all IMFs. The simulation and experimental results for three statistical indicators viz. kurtosis, Sr, Sα show that the proposed method based on variable cosine window is a powerful and reliable technique for fault diagnosis.
---
paper_title: Quantitative diagnosis of a spall-like fault of a rolling element bearing by empirical mode decomposition and the approximate entropy method

paper_content:

Abstract Spalling or pitting is the main manifestation of fault development in a bearing during the earlier stages. Previous studies indicated that the vibration signal of a bearing with a spall-like defect may be composed of two parts; the first part originates from the entry of the rolling element into the spall-like area, and the second part refers to the exit from the fault region. The quantitative diagnosis of a spall-like fault of the rolling element bearing can be realised if the entry–exit event times can be accurately calculated. However, the vibration signal of a faulty bearing is usually non-stationary and non-linear with strong background noise interference. Meanwhile, the signal energy from the early spall region is too low to accurately register the features of the entry–exit event in the time domain. In this work, the approximate entropy (ApEn) method and empirical mode decomposition (EMD) are applied to clearly separate the entry–exit events, and thus the size of the spall-like fault is estimated. First, the original acceleration vibration signal is decomposed by EMD, and some useful intrinsic mode function (IMF) components are obtained. Second, the concept of IMF-ApEn is introduced, which can directly reflect the complexity of the IMFs using the actual vibration signal. The IMF-ApEn distributions of different noise signals illustrate that the process of complexity changes when a full spectrum process is split into its IMFs. Third, a unit white noise IMF-ApEn distribution template serves as a sieve to extract the ( effective intrinsic mode functions ) EIMF components, and thus the entry and exit events in the response signal are distinguished. The IMF-ApEn method is further compared with a previous method (N. Sawalhi's method) to test its superiority. The dynamic effects are investigated when the ball element enters a spall-like region by computer simulation. The simulation and the experimental results show that the approach to the quantitative diagnosis of a rolling element bearing based on IMF-ApEn has higher veracity and good robustness.
---
paper_title: Rolling bearing diagnosing method based on Empirical Mode Decomposition of machine vibration signal

paper_content:

Abstract Rolling bearing faults are one of the major reasons for breakdown of industrial machinery and bearing diagnosing is one of the most important topics in machine condition monitoring. The main problem in industrial application of bearing vibration diagnostics is the masking of informative bearing signal by machine noise. The vibration signal of the rolling bearing is often covered or concealed by other structural vibrations sources, such as gears. Although a number of vibration diagnostic techniques have been developed over the last several years, in many cases these methods are quite complicated in use or only effective at later stages of damage development. This paper presents an EMD -based rolling bearing diagnosing method that shows potential for bearing damage detection at a much earlier stage of damage development. By using EMD a raw vibration signal is decomposed into a number of Intrinsic Mode Functions ( IMF s). Then, a new method of IMF s aggregation into three Combined Mode Functions ( CMF s) is applied and finally the vibration signal is divided into three parts of signal: noise-only part, signal-only part and trend-only part. To further bearing fault-related feature extraction from resultant signals, the spectral analysis of the empirically determined local amplitude is used. To validate the proposed method, raw vibration signals generated by complex mechanical systems employed in the industry (driving units of belt conveyors), including normal and fault bearing vibration data, are used in two case studies. The results show that the proposed rolling bearing diagnosing method can identify bearing faults at early stages of their development.
---
paper_title: Incipient fault diagnosis of roller bearings using empirical mode decomposition and correlation coefficient

paper_content:

In this paper, an incipient fault diagnosis method for roller bearings is proposed, based on empirical mode decomposition (EMD) and correlation coefficient (the normalized value of the cross-correlation function at the zero-lag point). The high frequency resonance phenomena begin to emerge as a defect forms gradually in the bearings. Therefore the high frequency content is sensitive to the change of the bearing conditions. Based on this, the EMD method is firstly applied to the bearing vibration signals to obtain some intrinsic mode functions (IMF) which contain different frequency bands from high to low. The first IMF of the signal to be detected, representing the high frequency band, is then selected to calculate the correlation coefficient between its frequency-domain signal and that of normal state. The correlation coefficient can demonstrate the fault evolution process and thus can detect an early fault. Finally the early faulty signals are analyzed by using the envelope analysis and the location of the fault is identified. The experimental results verify the effectiveness of the proposed method.
---
paper_title: Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings

paper_content:

This paper presents a novel signal processing scheme, bandwidth empirical mode decomposition, and adaptive multiscale morphological analysis (BEMD-AMMA) for early fault diagnosis of rolling bearings. In this scheme, we propose a bandwidth based method to select the best envelope interpolation method. First, multiple envelope algorithms are defined and separately subtracted from the original data to obtain the preintrinsic mode functions (PIMFs). Second, an IMF with the smallest frequency bandwidth is selected to be the optimal IMF (OIMF). Third, this OIMF is subtracted from the original signal, and then repeat the sifting process until the residual is a constant or monotonic. Since the OIMF has the smallest frequency bandwidth, the mode mixing phenomenon can be significantly weakened. After that the OIMFs with clear fault information are used to construct the main component of the original signal. Then, the AMMA is introduced to demodulate the constructed main component. Simulation and experimental vibration signals are employed to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms EMD-AMMA, ensemble empirical mode decomposition-AMMA, and generalized empirical mode decomposition-empirical envelope demodulation in detecting early inner race fault.
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paper_title: Health monitoring of cooling fan bearings based on wavelet filter

paper_content:

Abstract In this paper, a vibration-based health monitoring approach for cooling fans is proposed using a wavelet filter for early detection of faults in fan bearings and for the assessment of fault severity. To match the wavelet filter to the fault characteristic signal, a fuzzy rule is introduced to maximize the amplitudes of bearing characteristic frequencies (BCFs), which are an indicator of bearing faults. The sum of the amplitudes of BCFs and their harmonics (SABCF) is used as an index to capture the bearing degradation trend. A comparative study is conducted with commonly used time-domain indices in the degradation assessment, and performance is quantified by three measures, i.e., monotonicity, prognosability, and trendability. The analysis results of the experimental data show that the proposed method can effectively detect incipient defects and can better capture the degradation trend of fan bearings than traditional time-domain indices in vibration analysis.
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paper_title: Fault diagnosis of rolling bearing’s early weak fault based on minimum entropy de-convolution and fast Kurtogram algorithm

paper_content:

The rolling bearing’s early stage fault feature is very weak for reasons of the signal attenuation phenomenon between the fault source and the sensor collecting the fault signal and the interference of environment noise such as the rotor rotating frequency and its harmonics and so on. The feature extraction of rolling bearing’s early weak fault is not only very important but also very hard. The minimum entropy de-convolution and Fast Kurtogram algorithm are combined in the paper for rolling bearing’s early stage weak fault feature extraction. The effect of transmission path is de-convolved effectively, and the impulses are clarified using minimum entropy de-convolution technique firstly. Then the obtained signal by minimum entropy de-convolution is handled by the Fast Kurtogram algorithm and an optimal filter is established. At last the envelope de-modulation is applied on the filtered signal and better feature extraction result is obtained compared with the other methods such as wavelet transform, freque...
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paper_title: An Enhanced Extraction Method Based on EEMD for Processing a Bearing Vibration Signal With Multiple Vibration Sources

paper_content:

Condition monitoring and fault diagnosis for rolling element bearings is an imperative part for preventive maintenance procedures and reliability improvement of rotating machines. When a localized fault occurs at the early stage of real bearing failures, the impulses generated by the defect are relatively weak and usually overwhelmed by large noise and other higher-level macro-structural vibrations generated by adjacent machine components and machines. To indicate the bearing faulty state as early as possible, it is necessary to develop an effective signal processing method for extracting the weak bearing signal from a vibration signal containing multiple vibration sources. The ensemble empirical mode decomposition (EEMD) method inherits the advantage of the popular empirical mode decomposition (EMD) method and can adaptively decompose a multi-component signal into a number of different bands of simple signal components. However, the energy dispersion and many redundant components make the decomposition result obtained by the EEMD losing the physical significance. In this paper, to enhance the decomposition performance of the EEMD method, the similarity criterion and the corresponding combination technique are proposed to determine the similar signal components and then generate the real mono-component signals. To validate the effectiveness of the proposed method, it is applied to analyze raw vibration signals collected from two faulty bearings, each of which involves more than one vibration sources. The results demonstrate that the proposed method can accurately extract the bearing feature signal; meanwhile, it makes the physical meaning of each IMF clear.Copyright © 2014 by ASME
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paper_title: Noise resistant time frequency analysis and application in fault diagnosis of rolling element bearings

paper_content:

Abstract Rolling element bearings are frequently used in rotary machinery, but they are also fragile mechanical parts. Hence, exact condition monitoring and fault diagnosis for them plays an important role in ensuring machinery's reliable running. Timely diagnosis of early bearing faults is desirable, but the early fault signatures are easily submerged in noise. In this paper, Wigner–Ville spectrum based on cyclic spectral density (CSWVS for a brief notation) is studied, which is able to represent the cyclostationary signals while reducing the masking effect of additive stationary noise. Both simulations and experiments show that CSWVS is a noise resistant time frequency analysis technique for extracting bearing fault patterns, when bearing signals are under influences of random noise and gear vibrations. The 3-D feature of the CSWVS is proved useful in extracting bearing fault pattern from gearbox vibration signals, where bearing signals are affected by gear meshing vibration and noise. Besides, CSWVS utilizes the second order cyclostationary property of the vibration signals produced by bearing distributed fault, and clearly extracts its fault features, which cannot be extracted by envelope analysis. To quantitatively describe the extent of bearing fault, Renyi information encoded in the time frequency diagram of CSWVS is studied. It is shown to be a more sensitive index to reflect bearing performance degradation, compared with the spectral entropy (SE), squared envelope spectrum entropy (SESE) and Renyi informations for WVD, PWVD, especially when SNR is low.
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paper_title: Improving the weak feature extraction by adaptive stochastic resonance in cascaded piecewise-linear system and its application in bearing fault detection

paper_content:

In mechanical engineering field, early fault features are extremely weak and submerged in heavy noise, and the weak feature extraction is quite challenging. In this work, we apply the adaptive stochastic resonance in cascaded piecewise-linear system to extract the weak features. The adaptive stochastic resonance is realized by the quantum particle swarm algorithm. By optimizing system parameters, the efficiency of the feature extraction is improved greatly. As a result, the weak features can be easily extracted eventually. The effectiveness and the high-performance of the proposed method are verified by the numerical simulation and experimental data of rolling element bearings. The bearing fault under different motor loads is detected effectively, consequently confirming the robustness of the proposed method.
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paper_title: Applying the fuzzy lattice neurocomputing (FLN) classifier model to gear fault diagnosis

paper_content:

Gear faults are among the main causes of rotating machines breakdown in industrial applications. Intelligent condition monitoring for fault diagnosis can be helpful for detecting gear faults in an early stage so as to reduce production loss and, in addition, improve operation safety and reliability. In this work, we present an intelligent gear fault diagnosis scheme based on a novel classification model, namely the fuzzy lattice neurocomputing (FLN) classifier model. Five gear states including one healthy state and four defective states are tested in a two-stage gearbox. Statistical parameters in both the time domain and the frequency domain of vibration signals, acquired from gearbox, are used as features. We conducted experiments on a benchmark dataset as well as on a gear faults dataset to evaluate both the classification performance and the computational cost of the FLN classifier comparatively with alternative classification methods from the literature including artificial neural networks, support vector machines and decision trees. Our study has demonstrated that the FLN model yields better classification performance with smaller computational cost than the aforementioned alternative methods. The FLN classifier can further be used for condition monitoring and fault diagnosis in other mechanical systems.
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paper_title: Rolling element bearings diagnostics using the Symbolic Aggregate approXimation

paper_content:

Abstract Rolling element bearings are a very critical component in various engineering assets. Therefore it is of paramount importance the detection of possible faults, especially at an early stage, that may lead to unexpected interruptions of the production or worse, to severe accidents. This research work introduces a novel, in the field of bearing fault detection, method for the extraction of diagnostic representations of vibration recordings using the Symbolic Aggregate approXimation (SAX) framework and the related intelligent icons representation. SAX essentially transforms the original real valued time-series into a discrete one, which is then represented by a simple histogram form summarizing the occurrence of the chosen symbols/words. Vibration signals from healthy bearings and bearings with three different fault locations and with three different severity levels, as well as loading conditions, are analyzed. Considering the diagnostic problem as a classification one, the analyzed vibration signals and the resulting feature vectors feed simple classifiers achieving remarkably high classification accuracies. Moreover a sliding window scheme combined with a simple majority voting filter further increases the reliability and robustness of the diagnostic method. The results encourage the potential use of the proposed methodology for the diagnosis of bearing faults.
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paper_title: Early fault detection in gearboxes based on support vector machines and multilayer perceptron with a continuous wavelet transform

paper_content:

The study was undertaken to evaluate early fault detection of a gearbox using ANN.Selected coefficients from CWT were used as input data for the neural networks. One of the research problems investigated these days is early fault detection. To this end, advanced signal processing algorithms are employed. The present paper makes an attempt at early fault detection in a gearbox. In order to evaluate its technical condition, artificial neural networks were used. Early fault detection based on support vector machines is a relatively new and rarely employed method for evaluating condition of machines, particularly gearboxes. The available literature offers very promising results of using this method. In order to compare the obtained results, a multilayer perceptron network was created. Such standard neural network ensures high effectiveness. The vibration signal obtained from a sensor is seldom a material for direct analysis. First, it needs to be processed to bring out the informative part of the signal. To this end, a wavelet transform was used. The presented results concern both a "raw" vibration signal and processed one, investigated for two neural networks. The wavelet transform has proved to improve significantly the accuracy of condition evaluation and the results obtained by the two networks are consistent with one another.
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paper_title: Early detection of tooth crack damage in gearbox using empirical wavelet transform combined by Hilbert transform

paper_content:

Hilbert-Huang Transform (HHT) has been renowned for its capacity to reveal fault indicating information issue from vibration signals. It uses Empirical Mode Decomposition (EMD) to decompose a signal accordingly to its contained information into a set of Intrinsic Mode Functions (IMFs). Then, the instantaneous frequencies are performed of each IMF using Hilbert Transform (HT). However, the HHT has some disadvantages which are caused by the EMD technique. The EMD has the mode mixing problem that may occur between IMFs, it causes the End Effect phenomenon, which leads to a wrong instantaneous values at both sides of the signal. Furthermore, its lack of mathematical basis. To overcome the HHT inherent problems, we propose the use of the Empirical Wavelet Transform (EWT) which designs an appropriate wavelet filter bank fully depends on the processed signal with HT in the early detection and condition monitoring of tooth crack fault. In this paper, we develop a dynamic model describing a single stage spur gear ...
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paper_title: Maximal-overlap adaptive multiwavelet for detecting transient vibration responses from dynastic signal of rotating machineries

paper_content:

Vibration signal is an important prerequisite for mechanical fault detection. However, early stage defect of rotating machineries is difficult to identify because their incipient energy is interfered with background noises. Multiwavelet is a powerful tool used to conduct non-stationary fault feature extraction. However, the existing predetermined multiwavelet bases are independent of the dynamic response signals. In this paper, a constructing technique of vibration data-driven maximal-overlap adaptive multiwavelet (MOAMW) is proposed for enhancing the extracting performance of fault symptom. It is able to derive an optimal multiwavelet basis that best matches the critical non-stationary and transient fault signatures via genetic algorithm. In this technique, two-scale similarity transform (TST) and symmetric lifting (SymLift) scheme are combined to gain high designing freedom for matching the critical faulty vibration contents in vibration signals based on the maximal fitness objective. TST and SymLift can add modifications to the initial multiwavelet by changing the approximation order and vanishing moment of multiwavelet, respectively. Moreover, the beneficial feature of the MOAWM lies in that the maximal-overlap filterbank structure can enhance the periodic and transient characteristics of the sensor signals and preserve the time and frequency analyzing resolution during the decomposition process. The effectiveness of the proposed technique is validated via a numerical simulation as well as a rolling element bearing with an outer race scrape and a gearbox with rub fault.
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paper_title: Nonlinear Parameters for Monitoring Gear: Comparison Between Lempel-Ziv, Approximate Entropy, and Sample Entropy Complexity

paper_content:

Vibration analysis is the most used technique for defect monitoring failures of industrial gearboxes. Detection and diagnosis of gear defects are thus crucial to avoid catastrophic failures. It is therefore important to detect early fault symptoms. This paper introduces signal processing methods based on approximate entropy (ApEn), sample entropy (SampEn), and Lempel-Ziv Complexity (LZC) for detection of gears defects. These methods are based on statistical measurements exploring the regularity of vibratory signals. Applied to gear signals, the parameter selection of ApEn, SampEn, and LZC calculation is first numerically investigated, and appropriate parameters are suggested. Finally, an experimental study is presented to investigate the effectiveness of these indicators and a comparative study with traditional time domain indicators is presented. The results demonstrate that ApEn, SampEn, and LZC provide alternative features for signal processing. A new methodology is presented combining both Kurtosis and LZC for early detection of faults. The results show that this proposed method may be used as an effective tool for early detection of gear faults.
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paper_title: Multi-layer neural network with deep belief network for gearbox fault diagnosis

paper_content:

Identifying gearbox damage categories, especially for early faults and combined faults, is a challenging task in gearbox fault diagnosis. This paper presents multiple classifiers based on multi-layer neural networks (MLNN) to implement vibration signals for fault diagnosis in gearbox. A MLNN-based learning architecture using deep belief network (MLNNDBN) is proposed for gearbox fault diagnosis. Training process of the proposed learning architecture includes two stages: A deep belief network is constructed firstly, and then is trained; after a certain amount of epochs, the weights of deep belief network are used to initialize the weights of the constructed MLNN; at last, the trained MLNN is used as classifiers to classify gearbox faults. Multidimensional feature sets including time-domain, frequency-domain features are extracted to reveal gear health conditions. Experiments with different combined faults were conducted, and the vibration signals were captured under different loads and motor speeds. To confirm the superiority of MLNNDBN in fault classification, its performance is compared with other MLNN-based methods with different fine-tuning schemes and relevant vector machine. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery.
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paper_title: A multi-order FRFT self-adaptive filter based on segmental frequency fitting and early fault diagnosis in gears

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Abstract To effectively diagnose gear failure at an early stage, a multi-order Fractional Fourier transform (FRFT) self-adaptive filter based on segmental frequency fitting (MFSFF) is proposed to separate the feature components with curved frequency from the gearbox’s transient conditions. First, a linear multi-scale segmentation method (LMSS) is developed to divide the signal with curved frequency into segments with nearly linear frequency; then, a method for determining the FRFT filter parameters by fitting the frequency curve (DFFPFF) is developed to calculate the FRFT filter parameters for each signal segment, and the signal in each segment is filtered by an FRFT filter using these parameters to determine the MFSFF. The vibration of the gearbox’s acceleration and deceleration process is analyzed using an MFSFF and the filtered signal is demodulated. The experimental results show that LMSS is able to divide any signal with curved frequency into minimal segments with nearly linear frequency; DFFPFF is exact, fast, not influenced by the vibration source or the number of components, and able to determine the FRFT filter parameters for each signal segment accurately; the feature component of the gearbox’s transient condition is accurately extracted by an MFSFF, and the other components and noise are removed simultaneously. Early gear failure is diagnosed exactly by demodulation of the extracted feature component, which is difficult to identify using the traditional method.
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paper_title: Fast computation of the kurtogram for the detection of transient faults

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Abstract The kurtogram is a fourth-order spectral analysis tool recently introduced for detecting and characterising non-stationarities in a signal. The paradigm relies on the assertion that each type of transient is associated with an optimal (frequency/frequency resolution) dyad { f , Δ f } which maximises its kurtosis, and hence its detection. However, the complete exploration of the whole plane ( f , Δ f ) is a formidable task hardly amenable to on-line industrial applications. In this communication we describe a fast algorithm for computing the kurtogram over a grid that finely samples the ( f , Δ f ) plane. Its complexity is on the order of N log N , similarly to the FFT. The efficiency of the algorithm is then illustrated on several industrial cases concerned with the detection of incipient transient faults.
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paper_title: A joint sparse wavelet coefficient extraction and adaptive noise reduction method in recovery of weak bearing fault features from a multi-component signal mixture

paper_content:

Rolling element bearings are widely used to support rotating components of a machine. Due to close space locations of components in the machine, a vibration signal caused by bearing localized defects is easily overwhelmed by other strong vibration signals. Extracting the bearing fault signal from a multi-component signal mixture is thus significant to detect early bearing fault features and prevent machine breakdown. In this paper, a bearing fault diagnosis method, named cyclic spike detection method, is proposed to extract the weak bearing fault features from a multi-component signal mixture. Firstly, the optimal center frequency and bandwidth of a complex Morlet wavelet filter are determined by a simplex-simulated annealing algorithm along with a maximum sparsity objective function. The filtered signal is then obtained by applying the optimal wavelet filter to the multi-component signal mixture. After that, a new adaptive local maximum selection method is proposed to make the filtered signal succinct. Only a few spikes are retained to reveal potential cyclic intervals caused by bearing localized defects. Two multi-component signal mixtures, including a simulated signal and a real vibration signal collected from an industrial machine, are used to validate the effectiveness of the proposed cyclic spike detection method. The results demonstrate that the proposed method can extract the weak bearing fault features from other strong masking vibration signals and noise.
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paper_title: A Frequency-Weighted Energy Operator and complementary ensemble empirical mode decomposition for bearing fault detection

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Abstract Signal processing techniques for non-stationary and noisy signals have recently attracted considerable attentions. Among them, the empirical mode decomposition (EMD) which is an adaptive and efficient method for decomposing signals from high to low frequencies into intrinsic mode functions (IMFs). Ensemble EMD (EEMD) is proposed to overcome the mode mixing problem of the EMD. In the present paper, the Complementary EEMD (CEEMD) is used for bearing fault detection. As a noise-improved method, the CEEMD not only overcomes the mode mixing, but also eliminates the residual of added white noise persisting into the IMFs and enhance the calculation efficiency of the EEMD method. Afterward, a selection method is developed to choose relevant IMFs containing information about defects. Subsequently, a signal is reconstructed from the sum of relevant IMFs and a Frequency-Weighted Energy Operator is tailored to extract both the amplitude and frequency modulations from the selected IMFs. This operator outperforms the conventional energy operator and the enveloping methods, especially in the presence of strong noise and multiple vibration interferences. Furthermore, simulation and experimental results showed that the proposed method improves performances for detecting the bearing faults. The method has also high computational efficiency and is able to detect the fault at an early stage of degradation.
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paper_title: An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis

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Abstract Multi-fault identification is a challenge for rotating machinery fault diagnosis. The vibration signals measured from rotating machinery usually are complex, non-stationary and nonlinear. Especially, the useful multi-fault features are too weak to be identified at the early stage. In this paper, a novel method called improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis is proposed. Using multiwavelet packet as the pre-filter to improve EEMD decomposition results, multiwavelet packet decomposes the vibration signal into a series of narrow frequency bands and enhances the weak multi-fault characteristic components in the different narrow frequency bands. By selecting the proper added noise amplitude according to the vibration characteristics, EEMD is further improved to increase the accuracy and effectiveness of its decomposition results. The proposed method is applied to analyze the multi-fault of a blade rotor experimental setup and an industrial machine set, and the results confirm the advantage of the proposed method over EEMD, EEMD with multiwavelet packet, Hilbert–Huang transform and multiwavelet packet transform for multi-fault diagnosis.
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paper_title: Weak feature enhancement in machinery fault diagnosis using empirical wavelet transform and an improved adaptive bistable stochastic resonance

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Abstract Machinery vibration signal is a typical multi-component signal and fault features are often submerged by some interference components. To accurately extract fault features, a weak feature enhancement method based on empirical wavelet transform (EWT) and an improved adaptive bistable stochastic resonance (IABSR) is proposed. This method makes full use of the signal decomposition performance of EWT and the signal enhancement of the IABSR to achieve the purpose of fault feature enhancement in low frequency band of FFT spectrum. Firstly, EWT is used as the preprocessing program of bistable stochastic resonance (BSR) to decompose the machinery vibration signal into a set of sub-components. Then, the sensitive component that contains main fault information is further input into BSR system to enhance fault features with the assistance of residual noises. Finally, the fault features are identified from fast Fourier transform (FFT) spectrum of the BSR output. To achieve the optimal BSR output, the IABSR method based on salp swarm algorithm (SSA) is presented. Compared with the tradition adaptive BSR (ABSR), the IABSR optimizes not only the BSR system parameters but also the calculation step size. Two case studies on machinery fault diagnosis demonstrate the effectiveness and superiority of the proposed method. In addition, the proposed method is easy to implement and is robust to noise to some extent.
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paper_title: Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine

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Roller bearings are widely used in rotating machinery and one of the major reasons for machine breakdown is their failure. Vibration based condition monitoring is the most common method for extracting some important information to identify bearing defects. However, acquired acceleration signals are usually noisy, which significantly affects the results of fault diagnosis. Wavelet packet decomposition (WPD) is a powerful method utilized effectively for the denoising of the signals acquired. Furthermore, Ensemble empirical mode decomposition (EEMD) is a newly developed decomposition method to solve the mode mixing problem of empirical mode decomposition (EMD), which is a consequence of signal intermittence. In this study a combined automatic method is proposed to detect very small defects on roller bearings. WPD is applied to clean the noisy signals acquired, then informative feature vectors are extracted using the EEMD technique. Finally, the states of the bearings are examined by labeling the samples using the hyperplane constructed by the support vector machine algorithm. The data were generated by means of a test rig assembled in the labs of the Dynamics and Identification Research Group in the mechanical and aerospace engineering department, Politecnico di Torino. Various operating conditions (three shaft speeds, three external loads and a very small damage size on a roller) were considered to obtain reliable results. It is shown that the combined method proposed is able to identify the states of the bearings effectively.
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paper_title: Fuzzy determination of informative frequency band for bearing fault detection

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Detecting early faults in rolling element bearings is a crucial measure for the health maintenance of rotating machinery. As faulty features of bearings are usually demodulated into a high-frequency band, determining the informative frequency band (IFB) from the vibratory signal is a challenging task for weak fault detection. Existing approaches for IFB determination often divide the frequency spectrum of the signal into even partitions, one of which is regarded as the IFB by an individual selector. This work proposes a fuzzy technique to select the IFB with improvements in two aspects. On the one hand, an IFB-specific fuzzy clustering method is developed to segment the frequency spectrum into meaningful sub-bands. Considering the shortcomings of the individual selectors, on the other hand, three commonly-used selectors are combined using a fuzzy comprehensive evaluation method to guide the clustering. Among all the meaningful sub-bands, the one with the minimum comprehensive cost is determined as the IFB. The bearing faults, if any, can be detected from the demodulated envelope spectrum of the IFB. The proposed fuzzy technique was evaluated using both simulated and experimental data, and then compared with the state-of-the-art peer method. The results indicate that the proposed fuzzy technique is capable of generating a better IFB, and is suitable for detecting bearing faults.
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paper_title: Early Fault Diagnosis of Rotating Machinery by Combining Differential Rational Spline-Based LMD and K–L Divergence

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First, an improved local mean decomposition (LMD) method called differential rational spline-based LMD (DRS) is developed for signal decomposition. Differential and integral operations are introduced in LMD, which can weaken the mode mixing problem. Meanwhile, an optimized rational spline interpolation is proposed to calculate the envelope functions aiming to reduce the large errors caused by moving average in the traditional LMD. A series of product functions (PFs) is obtained after the application of the proposed DRS-LMD. Then, Kullback–Leibler (K–L) divergence is adopted to select main PF components that contain most fault information. The machine fault can be easily identified from the amplitude spectrum of the selected PF component. The effectiveness of the proposed DRS-LMD and K–L strategy is tested on simulated vibration signals and experimental vibration signals. Results show that the proposed method can increase the decomposition accuracy of the signals and can be used to detect early faults on the gears and rolling bearings.
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paper_title: Joint amplitude and frequency demodulation analysis based on local mean decomposition for fault diagnosis of planetary gearboxes

paper_content:

Abstract The vibration signals of faulty planetary gearboxes have complicated spectral structures due to the amplitude modulation and frequency modulation (AMFM) nature of gear damage induced vibration and the additional multiplicative amplitude modulation (AM) effect caused by the time-varying vibration transfer paths (for local gear damage case) and the passing planets (for distributed gear damage case). The spectral complexity leads to the difficulty in fault diagnosis of planetary gearboxes. Observing that both the amplitude envelope and the instantaneous frequency of planetary gearbox vibration signals are associated with the characteristic frequency of the faulty gear, a joint amplitude and frequency demodulation method is proposed for fault diagnosis of planetary gearboxes. In order to satisfy the mono-component requirement by instantaneous frequency estimation, a signal is firstly decomposed into product functions (PF) using the local mean decomposition (LMD) method. Then, the earliest extracted PF that has an instantaneous frequency fluctuating around the gear meshing frequency or its harmonics is chosen for further analysis, because it contains most of the information about the gear fault. The amplitude demodulation analysis can be accomplished through Fourier transforming the amplitude envelope of the chosen PF. For the frequency demodulation analysis, Fourier transform is applied to the estimated instantaneous frequency of the chosen PF to reveal its fluctuating frequency, thus obtaining the spectrum of the instantaneous frequency. By joint application of the amplitude and frequency demodulation methods, planetary gearbox faults can be diagnosed by matching the dominant peaks in the envelope spectrum and the spectrum of instantaneous frequency with the theoretical characteristic frequencies of faulty gears. The performance of the proposed method is illustrated by simulated signal analysis, and is validated by experimental signal analysis of a lab planetary gearbox with intentionally created pitting and naturally developed wear.
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paper_title: A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

paper_content:

Feature extraction is one of the most important, pivotal, and difficult problems in mechanical fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of early fault prediction. Therefore, a new fault feature extraction method, called the EDOMFE method based on integrating ensemble empirical mode decomposition (EEMD), mode selection, and multi-scale fuzzy entropy is proposed to accurately diagnose fault in this paper. The EEMD method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs) with a different physical significance. The correlation coefficient analysis method is used to calculate and determine three improved IMFs, which are close to the original signal. The multi-scale fuzzy entropy with the ability of effective distinguishing the complexity of different signals is used to calculate the entropy values of the selected three IMFs in order to form a feature vector with the complexity measure, which is regarded as the inputs of the support vector machine (SVM) model for training and constructing a SVM classifier (EOMSMFD based on EDOMFE and SVM) for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by real bearing vibration signals of the motor with different loads and fault severities. The experiment results show that the proposed EDOMFE method can effectively extract fault features from the vibration signal and that the proposed EOMSMFD method can accurately diagnose the fault types and fault severities for the inner race fault, the outer race fault, and rolling element fault of the motor bearing. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery.
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paper_title: Weak Fault Signal Detection of Rotating Machinery Based on Multistable Stochastic Resonance and VMD-AMD

paper_content:

For solving detection problems of multifrequency weak signals in noisy background, a novel weak signal detection method based on variational mode decomposition (VMD) and rescaling frequency-shifted multistable stochastic resonance (RFMSR) with analytical mode decomposition (AMD) is proposed. In this method, different signal frequency bands are processed by rescaling subsampling compression to make each frequency band meet the conditions of stochastic resonance. Before the enhanced signal components are synthesized, they are processed to achieve the enhanced signal by means of AMD, leaving only the enhanced sections of the signal. The processed signal is decomposed into intrinsic mode functions (IMF) by VMD, in order to require the detection of weak multifrequency signals. The experimental analysis of the rolling bearing inner ring fault and gear fault diagnosis demonstrate that the proposed method can not only enhance signal amplitude, reduce false components, and improve the VMD algorithm’s accuracy, but also effectively detect weak multifrequency signals submerged by noise.
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paper_title: Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform

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Abstract When a fault occurs on bearings, the measured bearing fault signals contain both high Q-factor oscillation component and low Q-factor periodic impact component. TQWT is the improvement of the traditional single Q-factor wavelet transform, which is very suitable for separating the low Q-factor component from the high Q-factor component. However, the accuracy of its decomposition heavily depended on the selection of Q-factors. There is no reported simple but effective method to select the Q-factors with enough accuracy. This study aims to develop a strategy to diagnostic the early fault of rolling bearings. In this paper, a characteristic frequency ratio (CFR) is used to optimize Q-factors of TQWT (OTQWT). However, directly application of OTQWT is difficult to extract fault signatures at early stage due to the weak fault symptoms and strong noise. A strategy of combination of intrinsic characteristic-scale decomposition (ICD) and TQWT is proposed. ICD owns significant advantages on computation efficiency and alleviation of mode mixing. The effectiveness of the proposed strategy is tested with both simulated and experimental vibration signals. Meanwhile, comparisons are conducted between the proposed method and other methods like: envelope demodulation and EEMD-TQWT. Results show that the proposed method has superior performance in extracting fault features of defective bearings at an early stage.
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paper_title: Early fault detection in automotive ball bearings using the minimum variance cepstrum

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Abstract Ball bearings in automotive wheels play an important role in a vehicle. They enable an automobile to run and simultaneously support the vehicle. Once faults are generated, even if they are small, they often grow fast even under normal driving condition and cause vibration and noise. Therefore, it is critical to detect faults as early as possible to prevent bearings from generating harsh noise and vibration. How early faults can be detected is associated with how well a detecting method finds the information of early faults from measured signal. Incipient faults are so small that the fault signal is inherently buried by noise. Minimum variance cepstrum (MVC) has been introduced for the observation of periodic impulse signal under noisy environments. We are particularly focusing on the definition of MVC that goes back to the original definition by Bogert et al. in comparison with the recently prevalent definition of cepstral analysis. In this work, the MVC is, therefore, obtained by liftering a logarithmic power spectrum, and the lifter bank is designed by the minimum variance algorithm. Furthermore, it is also shown how efficient the method is for detecting periodic fault signal made by early faults by using automotive ball bearings, with which an automobile is equipped under running conditions. We were able to detect incipient faults in 4 out of 12 normal bearings which passed acceptance test as well as in bearings that were recalled due to noise and vibration. In addition, we compared the results of the proposed method with results obtained using other older well-established early fault detection methods that were chosen from 4 groups of methods which were classified by the domain of observation. The results demonstrated that MVC determined bearing fault periods more clearly than other methods under the given condition.
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paper_title: Singular value decomposition based feature extraction approaches for classifying faults of induction motors

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Abstract This paper proposes singular value decomposition (SVD)-based feature extraction methods for fault classification of an induction motor: a short-time energy (STE) plus SVD technique in the time-domain analysis, and a discrete cosine transform (DCT) plus SVD technique in the frequency-domain analysis. To early identify induction motor faults, the extracted features are utilized as the inputs of multi-layer support vector machines (MLSVMs). Since SVMs perform well with the radial basis function (RBF) kernel for appropriately categorizing the faults of the induction motor, it is important to explore the impact of the σ values for the RBF kernel, which affects the classification accuracy. Likewise, this paper quantitatively evaluates the classification accuracy with different numbers of features, because the number of features affects the classification accuracy. According to the experimental results, although SVD-based features are effective for a noiseless environment, the STE plus SVD feature extraction approach is more effective with and without sensor noise in terms of the classification accuracy than the DCT plus SVD feature extraction approach. To demonstrate the improved classification of the proposed approach for identifying faults of the induction motor, the proposed SVD based feature extraction approach is compared with other state-of-the art methods and yields higher classification accuracies for both noiseless and noisy environments than conventional approaches.
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paper_title: Periodically correlated random processes: Application in early diagnostics of mechanical systems

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Abstract The covariance and spectral characteristics of periodically correlated random processes (PCRP) are used to describe the state of rotary mechanical systems and in their fault detection. The methods for estimation of mean function, covariance function, instantaneous spectral density and their Fourier coefficients for a given class of non-stationary random processes on the basis of experimental data, namely: the synchronous averaging, component, least squares method and linear filtration methods are considered. The first and second order periodicity detection methods are used for vibration signals analysis. A method for mechanical system fault identification and classification based on a harmonic series representation is developed. Examples of fault detection in rolling/sliding bearings and gearboxes are given.
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paper_title: A novel adaptive wavelet stripping algorithm for extracting the transients caused by bearing localized faults

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Rolling element bearings are widely used in rotating machinery. Its unexpected failure may result in machine breakdown. Whenever a bearing suffers a localized fault, the transients with a potential cyclic characteristic are generated by the rollers striking the localized fault. This phenomenon is an early bearing fault feature. Therefore, the extraction of the transients is beneficial to the identification of the early bearing fault. In this paper, a novel adaptive wavelet stripping algorithm (AWSA) is proposed to extract the simulated transients from an original bearing fault signal. Firstly, the parametric model of anti-symmetric real Laplace wavelet (ARLW) or impulse response wavelet (IRW) is built to approximate the real transients. Then, with the aid of wavelet correlation filtering analysis, the simulated transients with the optimal frequency, damping coefficient and delay time are adaptively peeled from the original bearing fault signal. The spatial reconstruction of the simulated transients reflects the random occurrence of the real transients. In order to boost the computing time of the AWSA, an enhanced AWSA is developed. At last, the bearing fault signals collected from an experimental machine and an industrial machine are used to validate the effectiveness of the AWSA. The results show that the AWSA can adaptively peel the simulated transients from the original bearing fault signals. A comparison with a periodic multi-transient model is conducted to show that the AWSA is better to extract the random characteristics of the real transients.
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paper_title: A frequency-shifted bispectrum for rolling element bearing diagnosis

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Abstract This paper is in the field of vibration based fault diagnosis for rolling element bearings, where one major issue is the ability to detect faults as early as possible. Bispectrum analysis has been applied in bearing diagnosis. In this paper, a frequency-shifted bispectrum is studied for bearing diagnosis. It is found that the frequency-shifted bispectrum is more suitable for analyzing amplitude modulated and frequency modulated signals than the bispectrum, when choosing the carrier frequency as the specific frequency shift value. For bearing vibration signals with amplitude modulation characteristics, in which the carrier frequencies are structural resonant frequencies, one of the structural resonances can be chosen as the specific frequency shift value for the frequency-shifted bispectrum analysis. To increase diagnostic ability of the frequency-shifted bispectrum for bearing faults, firstly, the frequency resolution is increased in the frequency-shifted bispectrum analysis through band pass filtering and frequency zooming; secondly, integration of the frequency-shifted bispectrum along one frequency variable is used to produce a one-dimensional spectral representation. Numerical simulation and experimental results show the feasibility of the proposed method for qualitatively and quantitatively diagnosing bearing faults.
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paper_title: Novel detection of local tooth damage in gears by the wavelet bicoherence

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Abstract A new technique, the instantaneous wavelet bicoherence (WB) is proposed and investigated. The use of the instantaneous and locally averaged WB from vibration measurements for local damage detection in gears is investigated for the first time; these bicoherences are better adapted than the classical Fourier bicoherence to the case of non-stationary signals. A new diagnostic feature based on the integrated modulus of the WB in a specific frequency range and a methodology for feature estimation are proposed. The WB techniques are applied to detection of a multiple “like natural” pitting on a back-to-back industrial spur gearbox system and natural pitting on a gear at test rig and show the possibility of early detection of local tooth faults. The detection effectiveness is evaluated by a local Fisher criterion estimated at each angular position of gear for the unpitted and pitted cases. The proposed WB-based diagnostic feature demonstrates robust experimental performance and superior detection capabilities (i.e., effective early damage detection differentially for teeth of the gear wheel) over the conventional detection methods based on the wavelet transform. The reason for this superior effectiveness is that the WB exploits the phase couplings of the wavelet transform at different frequencies, which contain useful additional information for detection of non-linear phenomena induced by local faults. The proposed approaches are compared with the two conventional approaches based on the wavelet transform.
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paper_title: Industrial drive fault diagnosis through vibration analysis using wavelet transform

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The fault diagnosis of electrical machines is significant to reduce the costs of maintenance through early detection of faults, which could be expensive to service. The idea of the research is that the advanced signal processing techniques called wavelet transform is exercised to haul out the faults from the vibration and current signal and a spectral component is being received to diagnose the condition of the machine. The existing system used wavelet transforms for the analysis of the stator faults and rotor faults are taken as a case study to prove the wavelet techniques for fault diagnosis. The proposed investigation of vibration analysis for the induction machines is done through frequency pattern using the decomposition of wavelet packet. The wavelet coefficients for the vibration have been extracted over a wide range of signals and the analysis could be percolated on the frequency domain through HAAR wavelet. In this paper, healthy and unhealthy motor with two broken ball bearings are used to estim...
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paper_title: Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing

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Abstract Rolling bearings are widely used in rotary machinery systems. The measured vibration signal of any part linked to rolling bearings contains fault information when failure occurs, differing only by energy levels. Bearing failure will cause the vibration of other components, and therefore the collected bearing vibration signals are mixed with vibration signal of other parts and noise. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can avoid the loss of local information. Subsequently using the multivariate empirical mode decomposition (multivariate EMD) to simultaneously analyze the multivariate signal is beneficial to extract fault information, especially for weak fault characteristics during the period of early failure. This paper proposes a novel method for fault feature extraction of rolling bearing based on multivariate EMD. The nonlocal means (NL-means) denoising method is used to preprocess the multivariate signal and the correlation analysis is employed to calculate fault correlation factors to select effective intrinsic mode functions (IMFs). Finally characteristic frequencies are extracted from the selected IMFs by spectrum analysis. The numerical simulations and applications to bearing monitoring verify the effectiveness of the proposed method and indicate that this novel method is promising in the field of signal decomposition and fault diagnosis.
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paper_title: Early chatter identification based on an optimized variational mode decomposition

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Abstract In the milling process, chatter, which results in poor surface quality, dimensional errors, and reduced cutter and machine life, is one of the main limitations on performance. Consequently, a reliable, real-time detection method is desired to recognize chatter while it is developing. This study develops a novel method of online chatter identification for milling processes. In this method, optimized variational mode decomposition (OVMD) is used to decompose cutting force measurements, and the sub-components containing chatter information are extracted using a simulated annealing (SA) algorithm. The approximate entropy and the sample entropy are used to detect the onset of chatter. To evaluate the effectiveness of the proposed method, milling operations were performed and force measurements were collected for five types of operating conditions. The results show that the proposed method is suitable for detecting both continuous and intermittent chatter. Rather than establishing an absolute threshold for chatter detection, the onset of chatter is identified from relative changes in the entropy with time that occur under the various cutting conditions. The proposed method is shown to have greater sensitivity and stability than empirical mode decomposition (EMD).
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paper_title: Enhanced Orthogonal Matching Pursuit Algorithm and Its Application in Mechanical Equipment Fault Diagnosis

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The vibration signal measured from the mechanical equipment is associated with the operation of key structure, such as the rolling bearing and gear. The effective signal processing method for early weak fault has attracted much attention and it is of vital importance in mechanical fault monitoring and diagnosis. The recently proposed atomic sparse decomposition algorithm is performed around overcomplete dictionary instead of the traditional signal analysis method using orthogonal basis operator. This algorithm has been proved to be effective in extracting useful components from complex signal by reducing influence of background noises. In this paper, an improved linear frequency-modulated (Ilfm) function as an atom is employed in the proposed enhanced orthogonal matching pursuit (EOMP) algorithm. Then, quantum genetic algorithm (QGA) with the OMP algorithm is integrated since the QGA can quickly obtain the global optimal solution of multiple parameters for rapidly and accurately extracting fault characteristic information from the vibration signal. The proposed method in this paper is superior to the traditional OMP algorithm in terms of accuracy and reducing the computation time through analyzing the simulation data and real world data. The experimental results based on the application of gear and bearing fault diagnosis indicate that it is more effective than traditional method in extracting fault characteristic information.
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paper_title: An Automatic Filtering Method Based on an Improved Genetic Algorithm—With Application to Rolling Bearing Fault Signal Extraction

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Fault feature extracted from fault signals plays a vital role in fault detection and diagnosis. However, in the early stage of defect, the fault signals (amplitudes and energy) are very weak and overwhelmed by large noise, and it is very difficult to extract fault feature effectively. To handle this problem, in this paper, an automatic filtering method based on an improved genetic algorithm (IGA) for extracting fault signals is presented. This method improves the binary coding generation of GA, so that each binary value matches a frequency of a fault signal frequency spectrum. Based on designed “statistical information evaluation function” as IGA fitness function, the similarity between normal state signal (noise signal) and IGA extracted noise signal is evaluated, and IGA employs genetic operations to search the optimal noise binary string. Negating noise binary string can obtain optimal fault signal binary string. It will then be multiplied by abnormal state vibration signal frequency spectrum, to yield a new frequency spectrum that only has fault components. This proposed method does not need to seek center frequency and bandwidth, and can automatically remove noise. The effective and advanced performance of the proposed method is evaluated by three different rolling bearing defects (outer race defect, inner race defect, and roller defect) in rolling bearing on rotating machine experimental bench and gearbox. Compared with rolling bearing fault characteristic frequency and results of denoising methods of bandpass filtering and wavelet decomposition, the proposed method can automatically filter noise and effectively extract fault signal. It proves that this method is feasible, effective, and advanced.
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paper_title: Variable cosine windowing of intrinsic mode functions: Application to gear fault diagnosis

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Gear pair is used for speed reduction or increasing torque and/or to change the direction of rotation. Gears are considered critical element in various mechanical systems. When gears are in use, the multi component vibration signals are generated. These vibration signals can be captured by mounting accelerometers at suitable locations. Vibration signal analysis is very effective tool in finding gear fault at early stage. The methods based on empirical mode decomposition (EMD) have been used for gear fault diagnosis in mechanical systems. The EMD method decomposes an original signal into different frequency-bands in time domain, known as intrinsic mode functions (IMFs). A serious problem in application of EMD is boundary distortion of IMFs. While doing statistical analysis of IMFs, boundary distortion may provide high values of statistical indicator (e.g. kurtosis, Sr, Sα), even if fault is not present. Several extension-based methods are employed to eliminate the boundary distortion problem. Extension-based methods cannot completely eliminate the boundary distortion, especially when the low-frequency component of the analyzed signal is weak. Recently, cosine window-based method has been proposed by which the boundary distortion can be controlled in boundaries of the signal and the middle component of it can be exactly decomposed into IMFs. The cosine window-based method works only for a particular IMF depending on the size of window. Since, in EMD process, the boundary distortion of successive IMFs increases, a variable cosine window is proposed in this paper to address the increasing boundary distortion problem. In the proposed method boundary distortion problem is minimized by using variable cosine window for all IMFs. The simulation and experimental results for three statistical indicators viz. kurtosis, Sr, Sα show that the proposed method based on variable cosine window is a powerful and reliable technique for fault diagnosis.
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paper_title: Incipient feature extraction for rolling element bearing based on particle filter preprocessing and kurtogram

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Fast kurtogram (FK) has been broadly investigated on fault diagnosis for rolling element bearing(REB) since it is put forward. But its performance is low as incipient feature extraction and fault diagnosis of REB as noise has greatly effect on the classification accuracy. How to effectively improve the signal-to-noise ratio(SNR) is important for characteristic frequency(CF) determination based on FK. In this research, the SNR can be improved by using particle filter(PF) to be satisfied with noise interference. It can be useful for the improvement of FK after de-noise. Firstly, state space function for the analyzing signal is constructed, background noise of origin signal can be extracted by using the PF and improve the SNR. Then, the optimal band is chosen by using FK after PF preprocessing. In the end, fault CF can be obtained by using spectrum analysis. Simulation and monitored vibration signals for REB are used to verify the effectiveness of this method. It can be concluded that the proposed method has better performance compared with that of FK and EMD-Kurtogram on REB fault early classification.
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paper_title: Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron

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Rolling element bearings are critical mechanical components in rotating machinery and fault detection in the early stages of damage is important to prevent their malfunctioning and failure. Vibration monitoring is the most widely used and cost-effective monitoring technique to detect, locate and distinguish faults in rolling element bearings. This paper purposes single hidden layer architecture for fault diagnosis of rolling element bearings. The particular of this proposed architecture is its ability to generalize for solving both basic classification and fault identification. The network uses the features of time-domain vibration signals with normal and defective bearings. The Multi Layer Perceptron (MLP) was trained and tested with a set of experimental data obtained from previous experiments developed by FEG, CWRU and RANDALL laboratories. The results show the effectiveness of the MLP to diagnose the machine condition for the various data used.
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paper_title: Effectiveness of Adaptive Filter Algorithms and Spectral Kurtosis in Bearing Faults Detection in a Gearbox

paper_content:

Bearing faults detection at the earliest stages is vital in avoiding future catastrophic failures. Many traditional techniques have been established and utilized in detecting bearing faults, though, these diagnostic techniques are not always successful when the bearing faults take place in gearboxes where the vibration signal is complex; under such circumstances it may be necessary to separate the bearing signal from the complex signal. The objective of this paper is to assess the effectiveness of an adaptive filter algorithms compared to a Spectral Kurtosis (SK) algorithm in diagnosing a bearing defects in a gearbox. Two adaptive filters have been used for the purpose of bearing signal separation, these algorithms were Least Mean Square (LMS) and Fast Block LMS (FBLMS) algorithms. These algorithms were applied to identify a bearing defects in a gearbox employed for an aircraft control system for which endurance tests were performed. The results show that the LMS algorithm is capable of detecting the bearing fault earlier in comparison to the other algorithms.
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paper_title: Sparse classification of rotating machinery faults based on compressive sensing strategy

paper_content:

Abstract Rotating machinery is integral to production processes and is composed of several components susceptible to malfunction. Safety and stable operation necessitate early identification of potential faults. Fault identification and classifications for rotating machinery are investigated in this study utilizing expanded monitoring data. A representation classification strategy for rotating machinery faults is developed based on a newly developed compressive sensing theory focusing on extraction and classification of fault features through sparse representation combined with random dimensionality reduction mapping. Original characteristics of a vibration signal are sampled and preserved by applying a small number of random projections and a learning redundant dictionary then constructed to sparsely represent the vibration signal. Fault signal impulse information is determined through an optimization strategy with sparsity promoting. A rolling bearing is utilized as an example in the study with simulations and experiments indicating that fault characteristics may be directly extracted from a small number of random projections without ever reconstructing the vibration signal completely and with minimal prior fault characteristic frequency knowledge required.
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paper_title: Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform

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Abstract When early weak fault emerges in rolling bearing the fault feature is too weak to extract using the traditional fault diagnosis methods such as Fast Fourier Transform (FFT) and envelope demodulation. The tunable Q-factor wavelet transform (TQWT) is the improvement of traditional one single Q-factor wavelet transform, and it is very fit for separating the low Q-factor transient impact component from the high Q-factor sustained oscillation components when fault emerges in rolling bearing. However, it is hard to extract the rolling bearing’ early weak fault feature perfectly using the TQWT directly. Ensemble empirical mode decomposition (EEMD) is the improvement of empirical mode decomposition (EMD) which not only has the virtue of self-adaptability of EMD but also overcomes the mode mixing problem of EMD. The original signal of rolling bearing’ early weak fault is decomposed by EEMD and several intrinsic mode functions (IMFs) are obtained. Then the IMF with biggest kurtosis index value is selected and handled by the TQWT subsequently. At last, the envelope demodulation method is applied on the low Q-factor transient impact component and satisfactory extraction result is obtained.
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paper_title: Incipient fault feature extraction of rolling bearings based on the MVMD and Teager energy operator

paper_content:

Aiming at the problems that the incipient fault of rolling bearings is difficult to recognize and the number of intrinsic mode functions (IMFs) decomposed by variational mode decomposition (VMD) must be set in advance and can not be adaptively selected, taking full advantages of the adaptive segmentation of scale spectrum and Teager energy operator (TEO) demodulation, a new method for early fault feature extraction of rolling bearings based on the modified VMD and Teager energy operator (MVMD-TEO) is proposed. Firstly, the vibration signal of rolling bearings is analyzed by adaptive scale space spectrum segmentation to obtain the spectrum segmentation support boundary, and then the number K of IMFs decomposed by VMD is adaptively determined. Secondly, the original vibration signal is adaptively decomposed into K IMFs, and the effective IMF components are extracted based on the correlation coefficient criterion. Finally, the Teager energy spectrum of the reconstructed signal of the effective IMF components is calculated by the TEO, and then the early fault features of rolling bearings are extracted to realize the fault identification and location. Comparative experiments of the proposed method and the existing fault feature extraction method based on Local Mean Decomposition and Teager energy operator (LMD-TEO) have been implemented using experimental data-sets and a measured data-set. The results of comparative experiments in three application cases show that the presented method can achieve a fairly or slightly better performance than LMD-TEO method, and the validity and feasibility of the proposed method are proved.
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paper_title: Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals

paper_content:

The implementation of condition monitoring and fault diagnosis system (CMFDS) on wind turbine is significant to lower the unscheduled breakdown. Generator is one of the most important components in wind turbine, and generator bearing fault identification always draws lots of attention. However, non-stationary vibration signal of weak fault and compound fault with a large amount of background noise makes this task challenging in many cases. So, effective signal processing method is essential in the accurate diagnosis step of CMFDS. As a novel signal processing method, empirical Wavelet Transform (EWT) is used to extract inherent modulation information by decomposing signal into mono-components under an orthogonal basis, which is seen as a powerful tool for mechanical fault diagnosis. Moreover, in order to avoid the inaccurate identification the internal modes caused by the heavy noise, wavelet spatial neighboring coefficient denoising with data-driven threshold is applied to increase Signal to Noise Ratio (SNR) before EWT. The effectiveness of the proposed technique on weak fault and compound fault diagnosis is first validated by two experimental cases. Finally, the proposed method has been applied to identify fault feature of generator bearing on wind turbine in wind farm successfully.
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paper_title: Diagnostics of gear faults using ensemble empirical mode decomposition, hybrid binary bat algorithm and machine learning algorithms

paper_content:

Early fault detection is a challenge in gear fault diagnosis. In particular, efficient feature extraction and feature selection is a key issue to automatic condition monitoring and fault diagnosis processes. In order to focus on those issues, this paper presents a study that uses ensemble empirical mode decomposition (EEMD) to extract features and hybrid binary bat algorithm (HBBA) hybridized with machine learning algorithm to reduce the dimensionality as well to select the predominant features which contains the necessary discriminative information. Efficiency of the approaches are evaluated using standard classification metrics such as Nearest neighbours, C4.5, DTNB, K star and JRip. The gear fault experiments were conducted, acquired the vibration signals for different gear states such as normal, frosting, pitting and crack, under constant motor speed and constant load. The proposed method is applied to identify the different gear faults at early stage and the results demonstrate its effectiveness.
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paper_title: An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

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In order to improve the effectiveness for identifying rolling bearing faults at an early stage, the present paper proposed a method that combined the so-called complementary ensemble empirical mode decomposition (CEEMD) method with a correlation theory for fault diagnosis of rolling element bearing. The cross-correlation coefficient between the original signal and each intrinsic mode function (IMF) was calculated in order to reduce noise and select an effective IMF. Using the present method, a rolling bearing fault experiment with vibration signals measured by acceleration sensors was carried out, and bearing inner race and outer race defect at a varying rotating speed with different degrees of defect were analyzed. And the proposed method was compared with several algorithms of empirical mode decomposition (EMD) to verify its effectiveness. Experimental results showed that the proposed method was available for detecting the bearing faults and able to detect the fault at an early stage. It has higher comp...
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paper_title: Performance Improvement of Ensemble Empirical Mode Decomposition for Roller Bearings Damage Detection

paper_content:

Ensemble empirical mode decomposition (EEMD) is a noise assisted method widely used for roller bearing damage detection. However, to successfully handle this technique still remains a great challenge: identification of two effective parameters (the amplitude of added noise and the number of ensemble trials), which affect the performances of the EEMD. Although a number of algorithms or values have been proposed, there is no robust guide to select optimal amplitude and the ensemble trial number yet, especially for early damage detection. In this study, a reliable method is proposed to determine the suitable amplitude and the proper number of trials is investigated as well. It is shown that the proposed method (performance improved EEMD) achieves higher damage detection success rate and creates larger Margin than the original algorithm. It leads to a substantially low trial numbers required to achieve perfect labelling of samples; in turn this fact leads to considerably less computational cost. The number of real vibration signals is analysed to verify effectiveness and robustness of the proposed method in discriminating and separating the faulty conditions.
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paper_title: Quantitative diagnosis of a spall-like fault of a rolling element bearing by empirical mode decomposition and the approximate entropy method

paper_content:

Abstract Spalling or pitting is the main manifestation of fault development in a bearing during the earlier stages. Previous studies indicated that the vibration signal of a bearing with a spall-like defect may be composed of two parts; the first part originates from the entry of the rolling element into the spall-like area, and the second part refers to the exit from the fault region. The quantitative diagnosis of a spall-like fault of the rolling element bearing can be realised if the entry–exit event times can be accurately calculated. However, the vibration signal of a faulty bearing is usually non-stationary and non-linear with strong background noise interference. Meanwhile, the signal energy from the early spall region is too low to accurately register the features of the entry–exit event in the time domain. In this work, the approximate entropy (ApEn) method and empirical mode decomposition (EMD) are applied to clearly separate the entry–exit events, and thus the size of the spall-like fault is estimated. First, the original acceleration vibration signal is decomposed by EMD, and some useful intrinsic mode function (IMF) components are obtained. Second, the concept of IMF-ApEn is introduced, which can directly reflect the complexity of the IMFs using the actual vibration signal. The IMF-ApEn distributions of different noise signals illustrate that the process of complexity changes when a full spectrum process is split into its IMFs. Third, a unit white noise IMF-ApEn distribution template serves as a sieve to extract the ( effective intrinsic mode functions ) EIMF components, and thus the entry and exit events in the response signal are distinguished. The IMF-ApEn method is further compared with a previous method (N. Sawalhi's method) to test its superiority. The dynamic effects are investigated when the ball element enters a spall-like region by computer simulation. The simulation and the experimental results show that the approach to the quantitative diagnosis of a rolling element bearing based on IMF-ApEn has higher veracity and good robustness.
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paper_title: Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System

paper_content:

Reliable monitoring for the early fault diagnosis of gearbox faults is of great concern for the wind industry. This paper presents a novel approach for health condition monitoring (CM) and fault diagnosis in wind turbine gearboxes using vibration analysis. This methodology is based on a machine learning algorithm that generates a baseline for the identification of deviations from the normal operation conditions of the turbine and the intrinsic characteristic-scale decomposition (ICD) method for fault type recognition. Outliers picked up during the baseline stage are decomposed by the ICD method to obtain the product components which reveal the fault information. The new methodology proposed for gear and bearing defect identification was validated by laboratory and field trials, comparing well with the methods reviewed in the literature.
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paper_title: Incipient Fault Feature Extraction for Rotating Machinery Based on Improved AR-Minimum Entropy Deconvolution Combined with Variational Mode Decomposition Approach

paper_content:

Aiming at the issue of extracting the incipient single-fault and multi-fault of rotating machinery from the nonlinear and non-stationary vibration signals with a strong background noise, a new fault diagnosis method based on improved autoregressive-Minimum entropy deconvolution (improved AR-MED) and variational mode decomposition (VMD) is proposed. Due to the complexity of rotating machinery systems, the periodic transient impulses of single-fault and multiple-faults always emerge in the acquired vibration signals. The improved autoregressive minimum entropy deconvolution (AR-MED) technique can effectively deconvolve the influence of the background noise, which aims to enhance the peak value of the multiple transient impulses. Nevertheless, the envelope spectrum of simulation and experimental in this work shows that there are many interference components exist on both left and right of fault characteristic frequencies when the background noise is strong. To overcome this shortcoming, the VMD is thus applied to adaptively decompose the filtered output vibration signal into a number of quasi-orthogonal intrinsic modes so as to better detect the single- and multiple-faults via those sub-band signals. The experimental and engineering application results demonstrate that the proposed method dramatically sharpens the fault characteristic frequencies (FCFs) from the impacts of bearing outer race and gearbox faults compared to the traditional methods, which show a significant improvement in early incipient faults of rotating machinery.
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paper_title: A new fault diagnosis algorithm for helical gears rotating at low speed using an optical encoder

paper_content:

Abstract Helical gears are widely used in gearboxes due to its low noise and high load carrying capacity, but it is difficult to diagnose their early faults based on the signals produced by condition monitoring systems, particularly when the gears rotate at low speed. In this paper, a new concept of Root Mean Square (RMS) value calculation using angle domain signals within small angular ranges is proposed. With this concept, a new diagnosis algorithm based on the time pulses of an encoder is developed to overcome the difficulty of fault diagnosis for helical gears at low rotational speeds. In this proposed algorithm, both acceleration signals and encoder impulse signal are acquired at the same time. The sampling rate and data length in angular domain are determined based on the rotational speed and size of the gear. The vibration signals in angular domain are obtained by re-sampling the vibration signal of the gear in the time domain according to the encoder pulse signal. The fault features of the helical gear at low rotational speed are then obtained with reference to the RMS values in small angular ranges and the order tracking spectrum following the Angular Domain Synchronous Average processing (ADSA). The new algorithm is not only able to reduce the noise and improves the signal to noise ratio by the ADSA method, but also extracts the features of helical gear fault from the meshing position of the faulty gear teeth, hence overcoming the difficulty of fault diagnosis of helical gears rotating at low speed. The experimental results have shown that the new algorithm is more effective than traditional diagnosis methods. The paper concludes that the proposed helical gear fault diagnosis method based on time pulses of encoder algorithm provides a new means of helical gear fault detection and diagnosis.
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paper_title: A heterogeneous fault diagnosis method for bearings in gearbox

paper_content:

Bearing failure is one of the most important causes of breakdown of rotating machinery. These failures can lead to catastrophic disasters or result in costly downtime. One of the key problems in bearing fault diagnosis is to detect the bearing fault as early as possible. This capability enables the operator to have enough time to do some preventive maintenance. Most papers investigate the bearing faults under rational assumption that bearings work individually. However, bearings are usually working as a part of complex systems like a gearbox. The fault signal of bearings can be easily masked by other vibration generated from gears and shafts. The proposed method separates bearing signals from other signals, and then the optimum frequency band which the bearing fault signal is prominent is determined by mean envelope Kurtosis. Subsequently, the envelope analysis is used to detect the bearing faults. Finally, two bearing fault experiments are used to validate the proposed method. Each experiment contains tw...
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paper_title: An investigation of rolling bearing early diagnosis based on high-frequency characteristics and self-adaptive wavelet de-noising

paper_content:

Rolling bearings are necessary parts in rotary machines. However, the problem of early fault diagnosis for rolling bearings is difficult to solve due to its low signal-to-noise ratio and non-linear and non-stationary signal. Based on a detailed investigation of rolling bearing vibration signals, this paper proposes a method for determining whether a fault occurs by comparing the high-frequency band power. If a fault occurs, we first de-noise the vibration signals using wavelet de-noising and then extract the fault characteristics in both the time domain and the time-frequency domain to avoid the limitations of using only one domain. Finally, the fault location is identified using the grey correlation method. According to the method application results, the recognition accuracy using the method proposed in this paper is satisfactory, proving that the method has superior performance.
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paper_title: An Improved VMD With Empirical Mode Decomposition and Its Application in Incipient Fault Detection of Rolling Bearing

paper_content:

Transient impulse analysis is an effective way to detect the bearing fault at its early stage. However, it is hard to precisely extract these so-called transient impulses because these collected vibration signals usually are non-stationary, nonlinear, and drowned by heavy background noise. Variational mode decomposition (VMD) can play the role as an adaptive signal processing tool to reveal the weak transient impulses from complex vibration signals. However, its reasonable mode number is difficult to pre-set and this would make the loss of useful transient impulses. To solve this issue, an improved VMD strategy is presented in this paper. For this method, it can not only utilize the advantages of traditional VMD and empirical mode decomposition (EMD) but also adaptively select sensitive intrinsic mode function (IMF) components for fault component analysis by proposed indexed values. EMD is first used to process the collected vibration signal into a series of IMFs, and the so-called useful IMFs are then evaluated by a sensitive IMF evaluation index which is based on the conjoint analysis of relatedness and kurtosis. Afterward, VMD is further improved to effectively decompose the denoised signal reconstructed from these selected useful IMFs of traditional EMD. Finally, the improved VMD is used for incipient fault diagnosis by a defined transient impulse monitoring index and Hilbert envelope analysis. Experiments are performed to demonstrate the effectiveness of the proposed method. The experimental results confirm that the proposed method can accurately extract the features of an incipient fault of a bearing.
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paper_title: Early Classification of Bearing Faults Using Morphological Operators and Fuzzy Inference

paper_content:

Bearing faults of rotating machinery are observed as impulses in the vibration signal, but it is mostly immersed in noise. In order to effectively remove this noise and detect the impulses, a novel technique with morphological operators and fuzzy inference is proposed in this paper. The effectiveness of the morphological operators lies with the correct selection of structuring elements (SEs). This paper also proposes a new algorithm for this SE selection based on kurtosis, thereby making the analysis free of empirical methods. When analyzed with three different sets of faults, the results show that this method is effective and robust in bringing out the impulses. With fuzzy inference being coupled to this new technique, it makes the algorithm to be able to detect early faults also.
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paper_title: Enhanced DET-Based Fault Signature Analysis for Reliable Diagnosis of Single and Multiple-Combined Bearing Defects

paper_content:

To early identify cylindrical roller bearing failures, this paper proposes a comprehensive bearing fault diagnosis method, which consists of spectral kurtosis analysis for finding the most informative subband signal well representing abnormal symptoms about the bearing failures, fault signature calculation using this subband signal, enhanced distance evaluation technique- (EDET-) based fault signature analysis that outputs the most discriminative fault features for accurate diagnosis, and identification of various single and multiple-combined cylindrical roller bearing defects using the simplified fuzzy adaptive resonance map (SFAM). The proposed comprehensive bearing fault diagnosis methodology is effective for accurate bearing fault diagnosis, yielding an average classification accuracy of 90.35%. In this paper, the proposed EDET specifically addresses shortcomings in the conventional distance evaluation technique (DET) by accurately estimating the sensitivity of each fault signature for each class. To verify the efficacy of the EDET-based fault signature analysis for accurate diagnosis, a diagnostic performance comparison is carried between the proposed EDET and the conventional DET in terms of average classification accuracy. In fact, the proposed EDET achieves up to 106.85% performance improvement over the conventional DET in average classification accuracy.
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paper_title: Rolling bearing diagnosing method based on Empirical Mode Decomposition of machine vibration signal

paper_content:

Abstract Rolling bearing faults are one of the major reasons for breakdown of industrial machinery and bearing diagnosing is one of the most important topics in machine condition monitoring. The main problem in industrial application of bearing vibration diagnostics is the masking of informative bearing signal by machine noise. The vibration signal of the rolling bearing is often covered or concealed by other structural vibrations sources, such as gears. Although a number of vibration diagnostic techniques have been developed over the last several years, in many cases these methods are quite complicated in use or only effective at later stages of damage development. This paper presents an EMD -based rolling bearing diagnosing method that shows potential for bearing damage detection at a much earlier stage of damage development. By using EMD a raw vibration signal is decomposed into a number of Intrinsic Mode Functions ( IMF s). Then, a new method of IMF s aggregation into three Combined Mode Functions ( CMF s) is applied and finally the vibration signal is divided into three parts of signal: noise-only part, signal-only part and trend-only part. To further bearing fault-related feature extraction from resultant signals, the spectral analysis of the empirically determined local amplitude is used. To validate the proposed method, raw vibration signals generated by complex mechanical systems employed in the industry (driving units of belt conveyors), including normal and fault bearing vibration data, are used in two case studies. The results show that the proposed rolling bearing diagnosing method can identify bearing faults at early stages of their development.
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paper_title: Wind Turbine Gearbox Fault Diagnosis Based on Improved EEMD and Hilbert Square Demodulation

paper_content:

The rapid expansion of wind farms has accelerated research into improving the reliability of wind turbines to reduce operational and maintenance costs. A critical component in wind turbine drive-trains is the gearbox, which is prone to different types of failures due to long-term operation under tough environments, variable speeds and alternating loads. To detect gearbox fault early, a method is proposed for an effective fault diagnosis by using improved ensemble empirical mode decomposition (EEMD) and Hilbert square demodulation (HSD). The method was verified numerically by implementing the scheme on the vibration signals measured from bearing and gear test rigs. In the implementation process, the following steps were identified as being important: (1) in order to increase the accuracy of EEMD, a criterion of selecting the proper resampling frequency for raw vibration signals was developed; (2) to select the fault related intrinsic mode function (IMF) that had the biggest kurtosis index value, the resampled signal was decomposed into a series of IMFs; (3) the selected IMF was demodulated by means of HSD, and fault feature information could finally be obtained. The experimental results demonstrate the merit of the proposed method in gearbox fault diagnosis.
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paper_title: An optimized variational mode decomposition for extracting weak feature of viscoelastic sandwich cylindrical structures

paper_content:

Variational mode decomposition (VMD), which is an alternative to empirical mode decomposition (EMD), has been widely used to extract the feature components of nonstationary signals. However, as a parameterized method, the performance of VMD is heavily influenced by its parameters. Meanwhile, it cannot efficiently extract weak feature components submerged in powerful ones. To address these problems, a novel method based on an optimized VMD is developed for precisely extracting the weak feature of viscoelastic sandwich cylindrical structures (VSCSs). In this method, a parameter optimization algorithm first is proposed to simultaneously select the crucial parameters in the VMD, and to reveal the characteristics of the influence of these parameters on the decomposition performance. Then, the weak feature components submerged in low-frequency strong ones are extracted twice by using the VMD with the optimized parameters. The effectiveness of the proposed method is verified by simulation signals and the experiment vibration signal collected from the VSCS. Its robustness to noise is also discussed. The results indicate that the parameter optimization algorithm can adaptively obtain optimal parameters, and compared with the optimized complementary ensemble EMD (CEEMD) and the original VMD, the proposed method can precisely extract the weak feature components submerged in strong ones.
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paper_title: Spectral kurtosis based on AR model for fault diagnosis and condition monitoring of rolling bearing

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Spectral kurtosis (SK) is an algorithm that gives an indication of how kurtosis varies with frequency. A frequency band that contains abundant information, especially the impact signal, can be tracked by calculating SK. In the present article, SK combined with Autoregressive AR model, was applied into the fault diagnosis and condition monitoring of bearings. Accelerated life test of rolling bearings in Hangzhou Bearing Test & Research Center (HBRC) was performed to collect vibration data over their entire lifetime (normal-fault-failure). The result shows that SK can detect early incipient fault by eliminating some other interfering frequency components. In addition, it can detect fault 5 min earlier than root mean value (RMS). This fault detection in advance is significant for condition monitoring.
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paper_title: Construction of adaptive redundant multiwavelet packet and its application to compound faults detection of rotating machinery

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It is significant to detect the fault type and assess the fault level as early as possible for avoiding catastrophic accidents. Due to diversity and complexity, the compound faults detection of rotating machinery under non-stationary operation turns to be a challenging task. Multiwavelet with two or more base functions may match two or more features of compound faults, which may supply a possible solution to compound faults detection. However, the fixed basis functions of multiwavelet transform, which are not related with the vibration signal, may reduce the accuracy of compound faults detection. Moreover, the decomposition results of multiwavelet transform not being own time-invariant is harmful to extract the features of periodical impulses. Furthermore, multiwavelet transform only focuses on the multi-resolution analysis in the low frequency band, and may leave out the useful features of compound faults. To overcome these shortcomings, a novel method called adaptive redundant multiwavelet packet (ARMP) is proposed based on the two-scale similarity transforms. Besides, the relative energy ratio at the characteristic frequency of the concerned component is computed to select the sensitive frequency bands of multiwavelet packet coefficients. The proposed method was used to analyze the compound faults of rolling element bearing. The results showed that the proposed method could enhance the ability of compound faults detection of rotating machinery.
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paper_title: Application of the horizontal slice of cyclic bispectrum in rolling element bearings diagnosis

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Abstract The vibration signals of rolling element bearings are random cyclostationary when they have faults. However, because the background noise is very heavy when the early fault occurs, it is difficult to disclose the latent periodic components successfully even using the second order cyclostationary analysis. To overcome this difficulty, the cyclic bispectrum (CBS), an alternative approach based on bispectrum and cyclostationarity analysis, is discussed in this paper. Furthermore, the slice spectrum analysis of the CBS is proposed. The CBS is a third-order cyclic statistical parameter, in the frequency domain. The CBS gives full play to the advantage, which is provided from the higher order cyclic statistical methods. It can restrain noise and provide more information than classical methods such as amplitude spectrum analysis and envelope analysis when the fault is at an early stage. However, the CBS is four-dimensional. So, the Slices Spectrum Analysis of the CBS is introduced to fault diagnosis. Firstly, the slice of the CBS, which is 3D structure, is sliced along the cyclic frequency axis. The CBS corresponding to one cyclic frequency is called the once slice of the CBS. However the information in the once slice is redundant and indirect for fault diagnosis. So the twice slice of the CBS, which is sliced along one frequency axis is studied. By analysis, the horizontal slice of the once slice of the CBS (HSCBS), which is at a special given cyclic frequency is proposed to resolve the contradiction. Simulation and experiment of the rolling element bearing fault diagnosis are performed, and the results indicate the feasibility and validity of the HSCBS analysis in rolling element bearing early fault diagnosis.
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paper_title: A new wind turbine fault diagnosis method based on the local mean decomposition

paper_content:

This paper proposed a novel wind turbine fault diagnosis method based on the local mean decomposition (LMD) technology. Wind energy is a renewable power source that produces no atmospheric pollution. The condition monitoring and fault diagnosis in wind turbine system are important in avoiding serious damage. Vibration analysis is a normal and useful technology in wind turbine condition monitoring and fault diagnosis. However, the relatively slow speed of the wind turbine components set a limitation in early fault diagnosis using vibration monitoring method. The traditional time-frequency analysis techniques have some drawbacks which make them not suitable for the nonlinear, non-Gaussian signal analysis. LMD is a new iterative approach to demodulate amplitude and frequency modulated signals, which is suitable for obtaining instantaneous frequencies in wind turbine condition monitoring and fault diagnosis. The experiment analysis of the wind turbine vibration signal proves the validity and availability of the new method.
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paper_title: A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery

paper_content:

Abstract Singular value decomposition (SVD), as an effective signal denoising tool, has been attracting considerable attention in recent years. The basic idea behind SVD denoising is to preserve the singular components (SCs) with significant singular values. However, it is shown that the singular values mainly reflect the energy of decomposed SCs, therefore traditional SVD denoising approaches are essentially energy-based, which tend to highlight the high-energy regular components in the measured signal, while ignoring the weak feature caused by early fault. To overcome this issue, a reweighted singular value decomposition (RSVD) strategy is proposed for signal denoising and weak feature enhancement. In this work, a novel information index called periodic modulation intensity is introduced to quantify the diagnostic information in a mechanical signal. With this index, the decomposed SCs can be evaluated and sorted according to their information levels, rather than energy. Based on that, a truncated linear weighting function is proposed to control the contribution of each SC in the reconstruction of the denoised signal. In this way, some weak but informative SCs could be highlighted effectively. The advantages of RSVD over traditional approaches are demonstrated by both simulated signals and real vibration/acoustic data from a two-stage gearbox as well as train bearings. The results demonstrate that the proposed method can successfully extract the weak fault feature even in the presence of heavy noise and ambient interferences.
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paper_title: Application of the wavelets multiresolution analysis and the high-frequency resonance technique for gears and bearings faults diagnosis

paper_content:

Defects diagnosis and condition surveillance of production and manufacturing rotating machinery in a plant is very important for guaranteeing production efficiency and plant safety. Condition surveillance for gear and bearing defects diagnosis for all rotating machines is a serious job because they cause accidents and consequently great production losses. For gear and bearing faults, and early detection especially in the gearboxes, researchers in the conditional maintenance and vibratory analysis used different methods and techniques in signal processing, among those and in full rise, demodulation by wavelets multiresolution analysis (WMRA) and high-frequency resonance technique (HFRT), based on the Hilbert transform, which allows filtering and the demodulation at the same time. In this paper, we propose to make a precise diagnosis for gears and bearings combined faults detection and identification in a laboratory test rig which simulate a rotating machine like in the manufacturing processes using WMRA and HFRT techniques. First of all, we applied WMRA method on simulated signals of gear or bearing defects or the combination of them, then we applied it on real signals measured on a test rig of the LMS laboratory in the University of Guelma.
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paper_title: Intelligent Diagnosis Method for Rotating Machinery Using Wavelet Transform and Ant Colony Optimization

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This paper proposes an intelligent diagnosis method for condition diagnosis of rotating machinery by using wavelet transform (WT) and ant colony optimization (ACO), in order to detect faults and distinguish fault types at an early stage. The WT is used to extract a feature signal of each machine state from a measured vibration signal for for high-accuracy condition diagnosis. The decision method of optimum frequency area for the extraction of the feature signal is discussed by using real plant data. We convert the state identification for the condition diagnosis of rotating machinery to a clustering problem of the values of the nondimensional symptom parameters (NSPs). ACO is introduced for this purpose. NSPs are calculated with the recomposed signals of each frequency level. These parameters can reflect the characteristics of the signals measured for the condition diagnosis. The synthetic detection index (SDI), on the basis of statistical theory, is defined to evaluate the applicability of the NSPs. The SDI can be used to select better NSPs for the ACO. Practical examples of diagnosis for a bearing used in the centrifugal fan system are shown to verify the effectiveness of the methods proposed in this paper.
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paper_title: Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis

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Abstract Bearing plays an essential role in the performance of mechanical system and fault diagnosis of mechanical system is inseparably related to the diagnosis of the bearings. However, it is a challenge to detect weak fault from the complex and non-stationary vibration signals with a large amount of noise, especially at the early stage. To improve the anti-noise ability and detect incipient fault, a novel fault detection method based on a short-time matching method and Support Vector Machine (SVM) is proposed. In this paper, the mechanism of roller bearing is discussed and the impact time frequency dictionary is constructed targeting the multi-component characteristics and fault feature of roller bearing fault vibration signals. Then, a short-time matching method is described and the simulation results show the excellent feature extraction effects in extremely low signal-to-noise ratio (SNR). After extracting the most relevance atoms as features, SVM was trained for fault recognition. Finally, the practical bearing experiments indicate that the proposed method is more effective and efficient than the traditional methods in weak impact signal oscillatory characters extraction and incipient fault diagnosis.
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paper_title: Online feature learning for condition monitoring of rotating machinery

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Abstract Condition-based maintenance of rotating machinery requires efficient condition monitoring methods that enable early detection of abnormal operational conditions and faults. This is a challenging problem because machines are different and change characteristics over time due to wear and maintenance. The efficiency and scalability of conventional condition monitoring methods are limited by the need for manual analysis and re-configuration. The problem to extract relevant features from condition monitoring signals and thereby detect and analyze changes in such signals is a central issue, which in principle can be addressed using machine learning methods. Former work demonstrates that dictionary learning can be used to automatically derive signal features that characterize different operational conditions and faults of a rotating machine, but the use of such methods for online condition monitoring purposes is an open problem. Here we investigate online learning of features using dictionary learning. We describe dictionary distance and signal fidelity based heuristics for anomaly detection, and we study the time-propagated features and sparse approximation of vibration and acoustic emission signals in three different case studies. We present results of numerical experiments with different hyperparameters affecting the approximation accuracy, computational cost, and the adaptation rate of the learned features. We find that the learned features change slowly under normal variations of the operational conditions in comparison to the rapid adaptation observed when a fault appears (bearing defects, magnetite particles in the lubricant, or plastic deformation of steel). Furthermore, we find that a sparse signal approximation with 2.5% preserved coefficients based on a propagated dictionary is sufficient for bearing defect detection.
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paper_title: Adaptive filtering based system for extracting gearbox condition feature from the measured vibrations

paper_content:

Vibration signals measured from a gearbox are complex multi-component signals, generated by tooth meshing, gear shaft rotation, gearbox resonance vibration signatures and a substantial amount of noise. This article presents a novel scheme for extracting gearbox fault features using adaptive filtering techniques for enhancing condition features, meshing frequency sidebands. A modified least mean square (LMS) algorithm is developed and validated using only one accelerometer, instead of using two accelerometers in traditional arrangement, as the main signal and a desired signal is artificially generated from the measured shaft speed and gear meshing frequencies. The proposed scheme is applied to a signal simulated from gearbox frequencies with a numerous values of step size. Findings confirm that 10−5 step size invariably produces more accurate results and there has been a substantial improvement in signal clarity (better signal-to-noise ratio); which make meshing frequency sidebands more discernible. The developed scheme is validated via a number of experiments carried out using two-stage helical gearbox for a pair of healthy gears and one pair suffering from a tooth breakage with severity fault 1 (25% tooth removal), and fault 2 (50% tooth removal) under loads (0%, and 80% of the total load). The experimental results show remarkable improvements and enhance gear fault features. This paper illustrates that the new approach offers a more effective way to detect early faults.
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paper_title: A Wavelet-Based Statistical Approach for Monitoring and Diagnosis of Compound Faults With Application to Rolling Bearings

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This paper proposes a wavelet-based statistical signal detection approach for monitoring and diagnosis of bearing compound faults at an early stage. The bearing vibration signal is decomposed by an orthonormal discrete wavelet transform to obtain its energy dispersions at multiple levels. We investigate the statistical properties of the decomposed signal energy under both the normal and faulty conditions, based on which a generalized likelihood ratio test is developed. An exponentially weighted moving average control chart is then constructed to detect faults at an early stage. Simulation studies and a real case study are conducted to demonstrate the effectiveness of the proposed method. Furthermore, the comparison studies show that the proposed method outperforms the empirical mode decomposition method and Hilbert envelope spectrum analysis method. Note to Practitioners —This paper is motivated by the problem of monitoring and diagnosis of compound faults in rolling bearings at the early stage, which are seldom considered in existing methods. In this paper, we propose a new approach by using statistical signal detection method and wavelet transform to handle the fault signals. This work aims at monitoring vibration signals and diagnosing fault types. Our simulation results show the proposed approach outperforms existing methods, especially at an early stage. Our future work will aim at improving the method’s sensitivity in distinguishing faults similar to each other.
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paper_title: Fault detection via recurrence time statistics and one-class classification

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A methodology for fault detection in rotating machinery is presented.An original one-class classifier based on extreme statistics (EVOC) is employed.One advantage of the method is a reduced number of hyperparameters to be adjusted.Another advantage is the use of only normal data of the machinery being monitored.The method shows higher classification accuracy than other state-of-the-art methods. Predictive maintenance has emerged as a fundamental practice to preserve production assets in many industrial environments. Of a wide set of approaches, vibration analysis is one of the most used for high-speed rotating machinery, especially when fault detection is to be automatic. Traditionally, this task has been studied as a classification problem using data extracted from the frequency domain. This approach, however, has two main limitations: (a) manufacture and mounting procedures can vary the vibration spectra of a machine, even when these share the same design; and (b) incipient fault signatures may be concealed in the frequency domain by noise and vibration from other parts of the system. For these reasons, the application of a classifier obtained for one machine to another machine is pointless, making early fault detection difficult. In this paper, a bearing fault detection problem is tackled using one-class classifiers and features extracted from vibration capture in the time domain using recurrence time statistics. We also describe a study of the behavior of the proposed method in real conditions. Our method shows high detection accuracy accompanied by a reduced number of false positives and negatives.
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paper_title: A novel approach to wavelet selection and tree kernel construction for diagnosis of rolling element bearing fault

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A novel methodology for early diagnosis of rolling element bearing fault is employed based on continuous wavelet transform (CWT) and support vector machine (SVM). CWT is especially suited for analyzing non-stationary signals in time–frequency domain where time information is retained as well as frequency content. To better approximate non-stationary vibration signals from rolling element bearing, a wavelet choice criterion is established to select an appropriate mother wavelet for feature extraction. The Shannon wavelet is picked out of several considered wavelets. The classification tree kernels (CTK) are constructed to address nonlinear classification of the characteristic samples derived from the wavelet coefficients. By using Fuzzy pruning strategy, a large variety of classification trees are generated. The trees with diverse structures can effectively explore intrinsic information among samples. Then, the tree kernel matrices can be acquired through ensemble statistical learning, which eventually reveal the similarity of samples objectively and stably. Under such architecture of kernel methods, a classification tree kernel based support vector machine (CTKSVM) is proposed to identify bearing fault. The performance of the methodology involving CWT and CTKSVM (CWT–CTKSVM) is evaluated by cross validation and independent test. The results show that the CWT–CTKSVM totally is superior to other SVM methods with common kernels. Therefore, it is a prospective technique for detection and identification of rolling element bearing fault.
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paper_title: Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis

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Vibration and acoustic analysis actively support the nondestructive and noninvasive fault diagnostics of rotating machines at early stages. Nonetheless, the acoustic signal is less used because of its vulnerability to external interferences, hindering an efficient and robust analysis for condition monitoring (CM). This paper presents a novel methodology to characterize different failure signatures from rotating machines using either acoustic or vibration signals. Firstly, the signal is decomposed into several narrow-band spectral components applying different filter bank methods such as empirical mode decomposition, wavelet packet transform, and Fourier-based filtering. Secondly, a feature set is built using a proposed similarity measure termed cumulative spectral density index and used to estimate the mutual statistical dependence between each bandwidth-limited component and the raw signal. Finally, a classification scheme is carried out to distinguish the different types of faults. The methodology is tested in two laboratory experiments, including turbine blade degradation and rolling element bearing faults. The robustness of our approach is validated contaminating the signal with several levels of additive white Gaussian noise, obtaining high-performance outcomes that make the usage of vibration, acoustic, and vibroacoustic measurements in different applications comparable. As a result, the proposed fault detection based on filter bank similarity features is a promising methodology to implement in CM of rotating machinery, even using measurements with low signal-to-noise ratio.
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paper_title: Fault detection and diagnosis in synchronous motors using hidden Markov model-based semi-nonparametric approach

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Early detection and diagnosis of faults in industrial machines would reduce the maintenance cost and also increase the overall equipment effectiveness by increasing the availability of the machinery systems. In this paper, a semi-nonparametric approach based on hidden Markov model is introduced for fault detection and diagnosis in synchronous motors. In this approach, after training the hidden Markov model classifiers (parametric stage), two matrices named probabilistic transition frequency profile and average probabilistic emission are computed based on the hidden Markov models for each signature (nonparametric stage) using probabilistic inference. These matrices are later used in forming a similarity scoring function, which is the basis of the classification in this approach. Moreover, a preprocessing method, named squeezing and stretching is proposed which rectifies the difficulty of dealing with various operating speeds in the classification process. Finally, the experimental results are provided and compared. Further investigations are carried out, providing sensitivity analysis on the length of signatures, the number of hidden state values, as well as statistical performance evaluation and comparison with conventional hidden Markov model-based fault diagnosis approach. Results indicate that implementation of the proposed preprocessing, which unifies the signatures from various operating speeds, increases the classification accuracy by nearly 21% and moreover utilization of the proposed semi-nonparametric approach improves the accuracy further by nearly 6%.
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paper_title: Automatic progressive damage detection of rotor bar in induction motor using vibration analysis and multiple classifiers

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There is an increased interest in developing reliable condition monitoring and fault diagnosis systems of machines like induction motors; such interest is not only in the final phase of the failure but also at early stages. In this paper, several levels of damage of rotor bars under different load conditions are identified by means of vibration signals. The importance of this work relies on a simple but effective automatic detection algorithm of the damage before a break occurs. The feature extraction is based on discrete wavelet analysis and autocorrelation process. Then, the automatic classification of the fault degree is carried out by a binary classification tree. In each node, comparing the learned levels of the breaking off correctly identifies the fault degree. The best results of classification are obtained employing computational intelligence techniques like support vector machines, multilayer perceptron, and the k-NN algorithm, with a proper selection of their optimal parameters.
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paper_title: The Delayed Correlation Envelope Analysis Technique Based on Sparse Signal Decomposition Method and its Application to Bearing early Fault Diagnosis

paper_content:

In order to extract the early weak fault information submerged in strong background noise of the bearing vibration signal, a delayed correlation envelope technique based on sparse signal decomposition method is proposed. This method can improve the signal to noise and extract the fault information efficiently. For the strong noise problem in the early fault, based on D-value of adjacent residual energy as the termination condition of the iterative method, reducing the noise effectively, and combine it with delayed correlation to enhance the denoising effect. The analysis results of roller bearing experimental data confirm the feasibility and validity of this method.
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paper_title: Single point bearing fault diagnosis using simplified frequency model

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The time synchronous averaging (TSA) is a well-known technique used for early detection of bearing failure in electrical machines. This method is very efficient if the characteristic default frequency is perfectly known. In this article, a reduced frequency model, derived from ESPRIT algorithm, is used to provide a very accurate estimation of the fault frequency. The precision obtained on this frequency allows to apply TSA algorithm under optimal conditions. The proposed method is tested on simulated and real vibration signals for inner and outer ring faults. Finally, a fault indicator is proposed to discriminate the healthy case from the faulty one.
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paper_title: Incipient fault diagnosis of roller bearings using empirical mode decomposition and correlation coefficient

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In this paper, an incipient fault diagnosis method for roller bearings is proposed, based on empirical mode decomposition (EMD) and correlation coefficient (the normalized value of the cross-correlation function at the zero-lag point). The high frequency resonance phenomena begin to emerge as a defect forms gradually in the bearings. Therefore the high frequency content is sensitive to the change of the bearing conditions. Based on this, the EMD method is firstly applied to the bearing vibration signals to obtain some intrinsic mode functions (IMF) which contain different frequency bands from high to low. The first IMF of the signal to be detected, representing the high frequency band, is then selected to calculate the correlation coefficient between its frequency-domain signal and that of normal state. The correlation coefficient can demonstrate the fault evolution process and thus can detect an early fault. Finally the early faulty signals are analyzed by using the envelope analysis and the location of the fault is identified. The experimental results verify the effectiveness of the proposed method.
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paper_title: Online unbalanced rotor fault detection of an IM drive based on both time and frequency domain analyses

paper_content:

Effective maintenance program provides incipient fault detection of rotating machines which reduces interim, unscheduled and excessive maintenance actions. By applying suitable online condition monitoring accompanied with signal processing techniques, machines irregularity can be detected at early stage. Therefore, this paper presents an online unbalanced rotor fault detection of an induction motor (IM). Characteristic features of motor current and vibration signals are analyzed in time domain as a fault diagnosis technique which is a key parameter to the fault threshold. Motor current and vibration signal analyses are also done based on Fast Fourier Transform (FFT), Hilbert Transform (HT) and Envelope Detection (ED) with low pass filter method to detect the severity of the fault and its possible location under different load conditions. The effectiveness of the electrical and mechanical fault detections based on FFT, HT and ED analyses are verified using the captured experimental data while the motor is running under different load conditions. The magnitudes of the spectral components are extracted for the pattern reorganization of the fault. Techniques are used under normal and unbalanced rotor conditions to a 3-phase, 2 pole, 0.246 KW, 60 Hz, 2950 rpm IM drive.
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paper_title: Study on Fault Diagnosis of Rolling Bearing Based on Time-Frequency Generalized Dimension

paper_content:

The condition monitoring technology and fault diagnosis technology of mechanical equipment played an important role in the modern engineering. Rolling bearing is the most common component of mechanical equipment which sustains and transfers the load. Therefore, fault diagnosis of rolling bearings has great significance. Fractal theory provides an effective method to describe the complexity and irregularity of the vibration signals of rolling bearings. In this paper a novel multifractal fault diagnosis approach based on time-frequency domain signals was proposed. The method and numerical algorithm of Multi-fractal analysis in time-frequency domain were provided. According to grid type J and order parameter q in algorithm, the value range of J and the cut-off condition of q were optimized based on the effect on the dimension calculation. Simulation experiments demonstrated that the effective signal identification could be complete by multifractal method in time-frequency domain, which is related to the factors such as signal energy and distribution. And the further fault diagnosis experiments of bearings showed that the multifractal method in time-frequency domain can complete the fault diagnosis, such as the fault judgment and fault types. And the fault detection can be done in the early stage of fault. Therefore, the multifractal method in time-frequency domain used in fault diagnosis of bearing is a practicable method.
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paper_title: Anomaly detection of cooling fan and fault classification of induction motor using Mahalanobis-Taguchi system

paper_content:

A health index, Mahalanobis distance (MD), is proposed to indicate the health condition of cooling fan and induction motor based on vibration signal. Anomaly detection and fault classification are accomplished by comparing MDs, which are calculated based on the feature data set extracted from the vibration signals under normal and abnormal conditions. Since MD is a non-negative and non-Gaussian distributed variable, Box-Cox transformation is used to convert the MDs into normal distributed variables, such that the properties of normal distribution can be employed to determine the ranges of MDs corresponding to different health conditions. Experimental data of cooling fan and induction motor are used to validate the proposed approach. The results show that the early stage failure of cooling fan caused by bearing generalized-roughness faults can be detected successfully, and the different unbalanced electrical faults of induction motor can be classified with a higher accuracy by Mahalanobis-Taguchi system. Such works could aid in the reliable operation of the machines, the reduction of the unexpected failures, and the improvement of the maintenance plan.
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paper_title: Application of local mean decomposition to the surveillance and diagnostics of low-speed helical gearbox

paper_content:

Abstract Gears are common power transmission elements and are frequently responsible for transmission failures. Instantaneous time-frequency spectrum (ITFS) resulted from local mean decomposition is applied to the surveillance and early fault diagnosis of a finishing rolling mill in this paper. Results of practical signals demonstrate that ITFS is effective and reliable for the early detection of gear local fault. In addition, a new parameter to evaluate the damage severity of the gearbox is also developed based on the marginal spectrum derived from ITFS. The utility of the new gear fault symptom has been investigated using practical vibration signals. Results show that the new parameter is only sensitive to the changes caused by the deterioration of a monitored unit and insensitive to the influence of the variable non-deterioration factors such as varying speed and loads. This new index may thus find its wide applications for machine prognostics in the near future.
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paper_title: Gear crack detection using modified TSA and proposed fault indicators for fluctuating speed conditions

paper_content:

Abstract Fault diagnosis of gearbox at an early stage is very important to avoid catastrophic failures. Condition indicators (CI) are used to quantify the level of vibration generated by the defected gears. A comprehensive comparison of various CIs, i.e., RMS, kurtosis, crest factor, FM0, FM4, M6A, NB4, energy ratio, NA4, energy operator and two new proposed condition indicators (PS-I and PS-II), have been performed for no crack, initial crack and advanced crack for different fluctuating profiles of input speed. This comparative analysis shows the responsiveness of indicators towards crack detection. Limited speed fluctuations and instantaneous high speed fluctuations are considered. Results suggest that newly proposed condition indicators are more robust, stable and effective towards crack detection under fluctuating profiles of speed. A modified time synchronous averaging (MTSA) is also proposed to increase signal-to-noise ratio (SNR).
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paper_title: Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery

paper_content:

Abstract Early fault detection and diagnosis plays an increasingly important role in various energy systems where it is critical to avoid deteriorating condition, degraded efficiency and unexpected failures. Rolling-element bearings are among the most common components of rotating machinery used for transformation of energy. Mechanical wear and defective bearings cause rotating machinery to decrease its efficiency, and thus increase energy consumption. A new technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis is presented. After normalization and the wavelet transform of vibration signals, the standard deviation as a measure of average energy and the logarithmic energy entropy as a measure of the degree of disorder are extracted in sub-bands of interest as representative features. Then the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection and diagnosis is performed by quadratic classifiers. Accuracy of the new technique was tested on four classes of the recorded vibrations signals, i.e. normal, with the fault of inner race, outer race and balls operation. An overall accuracy of 100% was achieved. The new technique will be further tested and implemented in a real production environment.
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paper_title: Knowledge extraction using data mining for multi-class fault diagnosis of induction motor

paper_content:

The feature extraction capability of rough set theory (RST) and genetic algorithm (GA) are used to extract knowledge from radial frame vibration signal for fault diagnosis of induction motor. This knowledge can assist in selecting scales for continuous wavelet transform (CWT) and mono-components required for Hilbert transform (HT) to extract fault related information from the vibration signal. Thus, the computational complexity of the signal processing tools is considerably reduced making both CWT and HT hardware friendly and suitable for real-time applications. For machine learning based automatic multi-class fault diagnosis, the performance of the classifiers are also considerably improved with significant reduction in computational burden since the redundant and irrelevant information can be effectively removed. The information obtained using data mining technique is successfully used to detect six types of induction motor faults. The results obtained are also verified in presence of high level of noise which has not been attempted earlier. The main contribution of the paper is to combine the advantages of two powerful signal processing tool like CWT and HT to extract hidden information from vibration signal in conjunction with data mining technique making them computationally efficient and easy to implement. A novel but simple approach to remove redundant and irrelevant information, to extract the important knowledge (fault features) using data mining.Knowledge generated using CWT are redundant whereas that from HT are mostly irrelevant. Useful knowledge extracted using RST and GA.Performances of the data mining techniques have been judged by taking into account their applicability in two different types of dataset.Only radial (vertical) frame vibration is found sufficient for multi-class fault diagnosis of induction motor.
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paper_title: Early fault detection of rotating machinery through chaotic vibration feature extraction of experimental data sets

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Abstract Fault detection of rotating machinery by the complex and non-stationary vibration signals with noise is very difficult, especially at the early stages. Also, many failure mechanisms and various adverse operating conditions in rotating machinery involve significant nonlinear dynamical properties. As a novel method, phase space reconstruction is used to study the effect of faults on the chaotic behavior, for the first time. Strange attractors in reconstructed phase space proof the existence of chaotic behavior. To quantify the chaotic vibration for fault diagnosis, a set of new features are extracted. These features include the largest Lyapunov exponent; approximate entropy and correlation dimension which acquire more fault characteristic information. The variations of these features for different healthy/faulty conditions are very good for fault diagnosis and identification. For the first time, a new chaotic feature space is introduced for fault detection, which is made from chaotic behavior features. In this space, different conditions of rotating machinery are separated very well. To obtain more generalized results, the features are introduced into a neural network to identify different faults in rotating machinery. The effectiveness of the new features based on chaotic vibrations is demonstrated by the experimental data sets. The proposed approach can reliably recognize different fault types and have more accurate results. Also, the performance of the new procedure is robust to the variation of load values and shows good generalization capability for various load values.
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paper_title: Bearing Fault Detection by One-Dimensional Convolutional Neural Networks

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Bearing faults are the biggest single source of motor failures. Artificial Neural Networks (ANNs) and other decision support systems are widely used for early detection of bearing faults. The typical decision support systems require feature extraction and classification as two distinct phases. Extracting fixed features each time may require a significant computational cost preventing their use in real-time applications. Furthermore, the selected features for the classification phase may not represent the most optimal choice. In this paper, the use of 1D Convolutional Neural Networks (CNNs) is proposed for a fast and accurate bearing fault detection system. The feature extraction and classification phases of the bearing fault detection are combined into a single learning body with the implementation of 1D CNN. The raw vibration data (signal) is fed into the proposed system as input eliminating the need for running a separate feature extraction algorithm each time vibration data is analyzed for classification. Implementation of 1D CNNs results in more efficient systems in terms of computational complexity. The classification performance of the proposed system with real bearing data demonstrates that the reduced computational complexity is achieved without a compromise in fault detection accuracy.
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paper_title: Novel method for rolling element bearing health assessment—A tachometer-less synchronously averaged envelope feature extraction technique

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Abstract The assessment and diagnosis of bearing health using vibration data has been a research topic of interest for many years and includes developments in an assortment of signal processing methods and classification algorithms. This paper investigates detecting bearing degradation at different levels of damage, in that estimating the bearing health at the various stages of degradation is important for predicting failure as well as making maintenance decisions. The proposed technique does not require a measure of the rotational shaft speed or bearing cage speed, which makes it very suitable in certain applications in which it is very difficult or not cost effective to measure the rotational speed. To effectively estimate the bearing health state, a novel tachometer-less synchronously averaged envelope (TLSAE) signal processing and feature extraction technique for rolling element bearing is proposed. The Tachometer-Less Synchronous Averaged Envelope (TLSAE) method consists of first using a narrow band pass filter around a calculated bearing fault frequency of interest and using the derivative of the phase of the Hilbert Transform of this narrow band signal to generate a synthesized tachometer signal that is representative of the impact due to a bearing defect. This synthesized tachometer signal is combined with the high frequency envelope method to perform synchronous averaging on the envelope signal, resulting in a defect synchronous envelope spectrum in which the frequency content is in terms of the fault frequency orders. The proposed method is further compared and evaluated with other existing methods, in particular to the traditional Fourier Transform technique, the bearing envelope analysis technique, and the empirical mode decomposition signal processing methods on the basis of whether each method provides an enhanced level of indication that can determine the health of rolling element bearings. Data from a bearing test-rig is used to facilitate the comparison and evaluation of the signal processing methods. Vibration data was collected from the test-rig for bearings with different levels of degradation. The calculated vibration features from the tachometer-less synchronously averaged envelope (TLSAE) technique are compared to the other feature extraction techniques; with the synchronous average method providing a set of bearing vibration features that can distinguish all three levels of damage on the outer race of the rolling element bearing. Future work looks to further investigate this proposed technique for data collected during a run to failure test in order to consider its merits for early detection of incipient bearing damage and whether it provides a consistent monotonic trend from spall initiation to bearing failure.
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paper_title: Vibration condition monitoring system for wind turbine bearings based on noise suppression with multi-point data fusion

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A noise suppression method for feature frequency extraction that is supplemented with multi-point data fusion was investigated in consideration of issues involving wind turbine vibration signals subject to high noise disturbance. The difficulty of extracting early weak fault features was examined as well. First, a de-noising and feature extraction method that uses EMD-Correlation was developed by adding empirical mode decomposition (EMD) and autocorrelation de-noising to the wavelet package transform under the effects of white noise and short-term disturbance noise in wind turbine vibration signals. Second, an EMD-Correlation analysis model for feature frequency extraction supplemented with multi-point data fusion was established with reference to adaptive resonance theory-2 to highlight the feature frequency of a possible early weak fault. Third, the results obtained with the actual and simulated fault vibration signals of wind turbine bearing faults and the outcomes of comparing the different feature frequency extraction methods show that the proposed method of EMD-Correlation that is supplemented with multi-point data fusion can not only effectively reduce white noise and short-term disturbance noise but can also extract the feature frequency of early weak faults. Finally, prototype hardware and software were developed for a wind turbine condition monitoring system based on the aforementioned fault feature extraction algorithms and tested in an actual wind turbine generation system.
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paper_title: Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings

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This paper presents a novel signal processing scheme, bandwidth empirical mode decomposition, and adaptive multiscale morphological analysis (BEMD-AMMA) for early fault diagnosis of rolling bearings. In this scheme, we propose a bandwidth based method to select the best envelope interpolation method. First, multiple envelope algorithms are defined and separately subtracted from the original data to obtain the preintrinsic mode functions (PIMFs). Second, an IMF with the smallest frequency bandwidth is selected to be the optimal IMF (OIMF). Third, this OIMF is subtracted from the original signal, and then repeat the sifting process until the residual is a constant or monotonic. Since the OIMF has the smallest frequency bandwidth, the mode mixing phenomenon can be significantly weakened. After that the OIMFs with clear fault information are used to construct the main component of the original signal. Then, the AMMA is introduced to demodulate the constructed main component. Simulation and experimental vibration signals are employed to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms EMD-AMMA, ensemble empirical mode decomposition-AMMA, and generalized empirical mode decomposition-empirical envelope demodulation in detecting early inner race fault.
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paper_title: Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR

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Abstract A significant number of failures of wind turbine drivetrains occur in the high-speed shaft bearings. In this paper, a vibration-based prognostic and health monitoring methodology for wind turbine high-speed shaft bearing (HSSB) is proposed using a spectral kurtosis (SK) data-driven approach. Indeed, time domain indices derived from SK are used and a comparative study is performed with frequently used time-domain features in the bearing degradation health assessment. The effectiveness is quantified by two measures, i.e., monotonicity and trendability. Among those features, the area under SK is utilized for the first time as a condition indicator of rolling bearing fault. A support vector regression (SVR) model was trained and tested for the prediction of the HSSB lifetime prognostics, showing the superiority of SK-derived indices of degradation assessment. We verified the potential of the prognostics method using real measured data from a drivetrain wind turbine. The experimental results show that the proposed approach can successfully detect an early failure and can better estimate the degradation trend of HSSB than traditional time-domain vibration features.
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paper_title: Sparse representation based latent components analysis for machinery weak fault detection

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Abstract Weak machinery fault detection is a difficult task because of two main reasons (1) At the early stage of fault development, signature of fault related component performs incompletely and is quite different from that at the apparent failure stage. In most instances, it seems almost identical with the normal operating state. (2) The fault feature is always submerged and distorted by relatively strong background noise and macro-structural vibrations even if the fault component already performs completely, especially when the structure of fault components and interference are close. To solve these problems, we should penetrate into the underlying structure of the signal. Sparse representation provides a class of algorithms for finding succinct representations of signal that capture higher-level features in the data. With the purpose of extracting incomplete or seriously overwhelmed fault components, a sparse representation based latent components decomposition method is proposed in this paper. As a special case of sparse representation, shift-invariant sparse coding algorithm provides an effective basis functions learning scheme for capturing the underlying structure of machinery fault signal by iteratively solving two convex optimization problems: an L1-regularized least squares problem and an L2-constrained least squares problem. Among these basis functions, fault feature can be probably contained and extracted if optimal latent component is filtered. The proposed scheme is applied to analyze vibration signals of both rolling bearings and gears. Experiment of accelerated lifetime test of bearings validates the proposed method׳s ability of detecting early fault. Besides, experiments of fault bearings and gears with heavy noise and interference show the approach can effectively distinguish subtle differences between defect and interference. All the experimental data are analyzed by wavelet shrinkage and basis pursuit de-noising (BPDN) method for comparison.
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paper_title: Analysing RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes

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Wind turbines (WTs) are designed to operate under extreme environmental conditions. This means that extreme and varying loads experienced by WT components need to be accounted for as well as gaining access to wind farms (WFs) at different times of the year. Condition monitoring (CM) is used by WF owners to assess WT health by detecting gearbox failures and planning for operations and maintenance (O&M). However, there are several challenges and limitations with commercially available CM technologies – ranging from the cost of installing monitoring systems to the ability to detect faults accurately. This study seeks to address some of these challenges by developing novel techniques for fault detection using the RMS and Extreme (peak) values of vibration signals. The proposed techniques are based on three models (signal correlation, extreme vibration, and RMS intensity) and have been validated with a time domain data driven approach using CM data of operational WTs. The findings of this study show that monitoring RMS and Extreme values serves as a leading indicator for early detection of faults using Extreme value theory, giving WF owners time to schedule O&M. Furthermore, it also indicates that the prediction accuracy of each CM technique depends on the physics of failure. This suggests that an approach which incorporates the strengths of multiple techniques is needed for holistic health assessment of WT components.
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paper_title: Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network

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Abstract After analyzing the shortcomings of current feature extraction and fault diagnosis technologies, a new approach based on wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) are combined to extract fault feature frequency and neural network for rotating machinery early fault diagnosis is proposed. Acquisition signals with fault frequency feature are decomposed into a series of narrow bandwidth using WPD method for de-noising, then, the intrinsic mode functions (IMFs), which usually denoted the features of corresponding frequency bandwidth can be obtained by applying EMD method. Thus, the component of IMF with signal feature can be separated from all IMFs and the energy moment of IMFs is proposed as eigenvector to effectively express the failure feature. The classical three layers BP neural network model taking the fault feature frequency as target input of neural network, the 5 spectral bandwidth energy of vibration signal spectrum as characteristic parameter, and the 10 types of representative rotor fault as output can be established to identify the fault pattern of a machine. Lastly, the fault identification model of rotating machinery with rotor lateral early crack based on BP neural network is taken as an example. The results show that the proposed method can effectively get the signal feature to diagnose the occurrence of early fault of rotating machinery.
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paper_title: Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance

paper_content:

Abstract Characterized by small size, light weight and large transmission ratio, planetary gear transmission is widely used in large scale complex mechanical system with low speed and heavy duty. However, due to the influences of operating condition, manufacturing error, assembly error and multi-tooth meshing, the vibration signal of planetary gear exhibits the characteristics of nonlinear and non-stationary. Especially when early gear fault occurs, the weak fault feature information is submerged in interfering signal. A weak fault feature information extraction method of planetary gear based on Ensemble Empirical Mode Decomposition (EEMD) and Adaptive Stochastic Resonance (ASR) is proposed. The original signal is decomposed to the Intrinsic Mode Functions (IMFs) with small modal aliasing by EEMD. The Signal to Noise Ratio (SNR) of fault feature frequency information of each IMF is calculated, and the IMFs with first four higher SNR are reconstructed and selected as the effective IMFs containing main fault feature information. ASR system is built by combining Particle Swarm Optimization (PSO) and Stochastic Resonance (SR). PSO algorithm is used to optimize the critical parameters of SR, and SNR of ASR output signal is defined as an optimization objective. When the signal reconstructed by effective IMFs is inputted into ASR system, the weak fault feature information can be extracted from the output signal of ASR system. The experimental results show that the proposed method can extract the weak fault feature information of normal gear and fault gears successfully. The amplitudes of fault feature frequency and its sidebands generated by planetary gear fault have a significantly increase, and the effects on sideband amplitudes of faults become even greater than that on the amplitude of fault feature frequency. For different gear faults, the amplitude of fault feature frequency has different changes, meanwhile different sidebands are produced. Planetary gear fault diagnosis can be achieved accurately by comparing the extracted weak fault feature information, so it is an effective method of weak fault feature information extraction of planetary gear.
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paper_title: Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive Fuzzy -Means Clustering

paper_content:

Vibration signal analysis is one of the most effective methods for mechanical fault diagnosis. Available part of the information is always concealed in component noise, which makes it much more difficult to detect the defection, especially at early stage of the development. This paper presents a new approach for mechanical fault diagnosis based on time domain analysis and adaptive fuzzy -means clustering. By analyzing vibration signal collected, nine common time domain parameters are calculated. This lot of data constitutes data matrix as characteristic vectors to be detected. And using adaptive fuzzy -means clustering, the optimal clustering number can be gotten then to recognize different fault types. Moreover, five parameters, including variance, RMS, kurtosis, skewness, and crest factor, of the nine are selected as the new eigenvector matrix to be clustered for more optimal clustering performance. The test results demonstrate that the proposed approach has a sensitive reflection towards fault identifications, including slight fault.
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paper_title: Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution

paper_content:

The early fault characteristics of rolling element bearings carried by vibration signals are quite weak because the signals are generally masked by heavy background noise. To extract the weak fault characteristics of bearings from the signals, an improved spectral kurtosis (SK) method is proposed based on maximum correlated kurtosis deconvolution (MCKD). The proposed method combines the ability of MCKD in indicating the periodic fault transients and the ability of SK in locating these transients in the frequency domain. A simulation signal overwhelmed by heavy noise is used to demonstrate the effectiveness of the proposed method. The results show that MCKD is beneficial to clarify the periodic impulse components of the bearing signals, and the method is able to detect the resonant frequency band of the signal and extract its fault characteristic frequency. Through analyzing actual vibration signals collected from wind turbines and hot strip rolling mills, we confirm that by using the proposed method, it is possible to extract fault characteristics and diagnose early faults of rolling element bearings. Based on the comparisons with the SK method, it is verified that the proposed method is more suitable to diagnose early faults of rolling element bearings.
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paper_title: Recognition of rolling bearing fault patterns and sizes based on two-layer support vector regression machines

paper_content:

The fault diagnosis of rolling element bearings has drawn considerable research attention in recent years because these fundamental elements frequently suffer failures that could result in unexpected machine breakdowns. Artificial intelligence algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) have been widely investigated to identify various faults. However, as the useful life of a bearing deteriorates, identifying early bearing faults and evaluating their sizes of development are necessary for timely maintenance actions to prevent accidents. This study proposes a new two-layer structure consisting of support vector regression machines (SVRMs) to recognize bearing fault patterns and track the fault sizes. The statistical parameters used to track the fault evolutions are first extracted to condense original vibration signals into a few compact features. The extracted features are then used to train the proposed two-layer SVRMs structure. Once these parameters of the proposed two-layer SVRMs structure are determined, the features extracted from other vibration signals can be used to predict the unknown bearing health conditions. The effectiveness of the proposed method is validated by experimental datasets collected from a test rig. The results demonstrate that the proposed method is highly accurate in differentiating between fault patterns and determining their fault severities. Further, comparisons are performed to show that the proposed method is better than some existing methods.
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paper_title: A novel fault diagnosis method for rotating machinery based on S transform and morphological pattern spectrum

paper_content:

With the continuing expansion of the applications of rotating machinery, an earlier and more accurate fault diagnosis method is required. In this paper, a novel characterization method based on S transform and morphological pattern spectrum (ST-MPS) was put forward. In order to verify the application of the method, ST-MPS was applied to a set of experimental signals obtained in a bearing test bench, and the results verified that the proposed feature extraction method is an effective approach to accurately classify the types of bearing fault.
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paper_title: Detection of weak fault using sparse empirical wavelet transform for cyclic fault

paper_content:

The successful prediction of the remaining useful life of rolling element bearings depends on the capability of early fault detection. A critical step in fault diagnosis is to use the correct signal processing techniques to extract the fault signal. This paper proposes a newly developed diagnostic model using a sparse-based empirical wavelet transform (EWT) to enhance the fault signal to noise ratio. The unprocessed signal is first analyzed using the kurtogram to locate the fault frequency band and filter out the system noise. Then, the preprocessed signal is filtered using the EWT. The lq-regularized sparse regression is implemented to obtain a sparse solution of the defect signal in the frequency domain. The proposed method demonstrates a significant improvement of the signal to noise ratio and is applicable for detection of cyclic fault, which includes the extraction of the fault signatures of bearings and gearboxes.
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paper_title: Automatic bearing fault diagnosis based on one-class ν-SVM

paper_content:

Rolling-element bearings are among the most used elements in industrial machinery, thus an early detection of a defect in these components is necessary to avoid major machine failures. Vibration analysis is a widely used condition monitoring technique for high-speed rotating machinery. Using the information contained in the vibration signals, an automatic method for bearing fault detection and diagnosis is presented in this work. Initially, a one-class @n-SVM is used to discriminate between normal and faulty conditions. In order to build a model of normal operation regime, only data extracted under normal conditions is used. Band-pass filters and Hilbert Transform are then used sequentially to obtain the envelope spectrum of the original raw signal that will finally be used to identify the location of the problem. In order to check the performance of the method, two different data sets are used: (a) real data from a laboratory test-to-failure experiment and (b) data obtained from a fault-seeded bearing test. The results showed that the method was able not only to detect the failure in an incipient stage but also to identify the location of the defect and qualitatively assess its evolution over time.
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paper_title: An Enhanced Extraction Method Based on EEMD for Processing a Bearing Vibration Signal With Multiple Vibration Sources

paper_content:

Condition monitoring and fault diagnosis for rolling element bearings is an imperative part for preventive maintenance procedures and reliability improvement of rotating machines. When a localized fault occurs at the early stage of real bearing failures, the impulses generated by the defect are relatively weak and usually overwhelmed by large noise and other higher-level macro-structural vibrations generated by adjacent machine components and machines. To indicate the bearing faulty state as early as possible, it is necessary to develop an effective signal processing method for extracting the weak bearing signal from a vibration signal containing multiple vibration sources. The ensemble empirical mode decomposition (EEMD) method inherits the advantage of the popular empirical mode decomposition (EMD) method and can adaptively decompose a multi-component signal into a number of different bands of simple signal components. However, the energy dispersion and many redundant components make the decomposition result obtained by the EEMD losing the physical significance. In this paper, to enhance the decomposition performance of the EEMD method, the similarity criterion and the corresponding combination technique are proposed to determine the similar signal components and then generate the real mono-component signals. To validate the effectiveness of the proposed method, it is applied to analyze raw vibration signals collected from two faulty bearings, each of which involves more than one vibration sources. The results demonstrate that the proposed method can accurately extract the bearing feature signal; meanwhile, it makes the physical meaning of each IMF clear.Copyright © 2014 by ASME
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paper_title: A Frequency-Weighted Energy Operator and complementary ensemble empirical mode decomposition for bearing fault detection

paper_content:

Abstract Signal processing techniques for non-stationary and noisy signals have recently attracted considerable attentions. Among them, the empirical mode decomposition (EMD) which is an adaptive and efficient method for decomposing signals from high to low frequencies into intrinsic mode functions (IMFs). Ensemble EMD (EEMD) is proposed to overcome the mode mixing problem of the EMD. In the present paper, the Complementary EEMD (CEEMD) is used for bearing fault detection. As a noise-improved method, the CEEMD not only overcomes the mode mixing, but also eliminates the residual of added white noise persisting into the IMFs and enhance the calculation efficiency of the EEMD method. Afterward, a selection method is developed to choose relevant IMFs containing information about defects. Subsequently, a signal is reconstructed from the sum of relevant IMFs and a Frequency-Weighted Energy Operator is tailored to extract both the amplitude and frequency modulations from the selected IMFs. This operator outperforms the conventional energy operator and the enveloping methods, especially in the presence of strong noise and multiple vibration interferences. Furthermore, simulation and experimental results showed that the proposed method improves performances for detecting the bearing faults. The method has also high computational efficiency and is able to detect the fault at an early stage of degradation.
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paper_title: An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis

paper_content:

Abstract Multi-fault identification is a challenge for rotating machinery fault diagnosis. The vibration signals measured from rotating machinery usually are complex, non-stationary and nonlinear. Especially, the useful multi-fault features are too weak to be identified at the early stage. In this paper, a novel method called improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis is proposed. Using multiwavelet packet as the pre-filter to improve EEMD decomposition results, multiwavelet packet decomposes the vibration signal into a series of narrow frequency bands and enhances the weak multi-fault characteristic components in the different narrow frequency bands. By selecting the proper added noise amplitude according to the vibration characteristics, EEMD is further improved to increase the accuracy and effectiveness of its decomposition results. The proposed method is applied to analyze the multi-fault of a blade rotor experimental setup and an industrial machine set, and the results confirm the advantage of the proposed method over EEMD, EEMD with multiwavelet packet, Hilbert–Huang transform and multiwavelet packet transform for multi-fault diagnosis.
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paper_title: Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform

paper_content:

Abstract When early weak fault emerges in rolling bearing the fault feature is too weak to extract using the traditional fault diagnosis methods such as Fast Fourier Transform (FFT) and envelope demodulation. The tunable Q-factor wavelet transform (TQWT) is the improvement of traditional one single Q-factor wavelet transform, and it is very fit for separating the low Q-factor transient impact component from the high Q-factor sustained oscillation components when fault emerges in rolling bearing. However, it is hard to extract the rolling bearing’ early weak fault feature perfectly using the TQWT directly. Ensemble empirical mode decomposition (EEMD) is the improvement of empirical mode decomposition (EMD) which not only has the virtue of self-adaptability of EMD but also overcomes the mode mixing problem of EMD. The original signal of rolling bearing’ early weak fault is decomposed by EEMD and several intrinsic mode functions (IMFs) are obtained. Then the IMF with biggest kurtosis index value is selected and handled by the TQWT subsequently. At last, the envelope demodulation method is applied on the low Q-factor transient impact component and satisfactory extraction result is obtained.
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paper_title: An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

paper_content:

In order to improve the effectiveness for identifying rolling bearing faults at an early stage, the present paper proposed a method that combined the so-called complementary ensemble empirical mode decomposition (CEEMD) method with a correlation theory for fault diagnosis of rolling element bearing. The cross-correlation coefficient between the original signal and each intrinsic mode function (IMF) was calculated in order to reduce noise and select an effective IMF. Using the present method, a rolling bearing fault experiment with vibration signals measured by acceleration sensors was carried out, and bearing inner race and outer race defect at a varying rotating speed with different degrees of defect were analyzed. And the proposed method was compared with several algorithms of empirical mode decomposition (EMD) to verify its effectiveness. Experimental results showed that the proposed method was available for detecting the bearing faults and able to detect the fault at an early stage. It has higher comp...
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paper_title: Performance Improvement of Ensemble Empirical Mode Decomposition for Roller Bearings Damage Detection

paper_content:

Ensemble empirical mode decomposition (EEMD) is a noise assisted method widely used for roller bearing damage detection. However, to successfully handle this technique still remains a great challenge: identification of two effective parameters (the amplitude of added noise and the number of ensemble trials), which affect the performances of the EEMD. Although a number of algorithms or values have been proposed, there is no robust guide to select optimal amplitude and the ensemble trial number yet, especially for early damage detection. In this study, a reliable method is proposed to determine the suitable amplitude and the proper number of trials is investigated as well. It is shown that the proposed method (performance improved EEMD) achieves higher damage detection success rate and creates larger Margin than the original algorithm. It leads to a substantially low trial numbers required to achieve perfect labelling of samples; in turn this fact leads to considerably less computational cost. The number of real vibration signals is analysed to verify effectiveness and robustness of the proposed method in discriminating and separating the faulty conditions.
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paper_title: Wind Turbine Gearbox Fault Diagnosis Based on Improved EEMD and Hilbert Square Demodulation

paper_content:

The rapid expansion of wind farms has accelerated research into improving the reliability of wind turbines to reduce operational and maintenance costs. A critical component in wind turbine drive-trains is the gearbox, which is prone to different types of failures due to long-term operation under tough environments, variable speeds and alternating loads. To detect gearbox fault early, a method is proposed for an effective fault diagnosis by using improved ensemble empirical mode decomposition (EEMD) and Hilbert square demodulation (HSD). The method was verified numerically by implementing the scheme on the vibration signals measured from bearing and gear test rigs. In the implementation process, the following steps were identified as being important: (1) in order to increase the accuracy of EEMD, a criterion of selecting the proper resampling frequency for raw vibration signals was developed; (2) to select the fault related intrinsic mode function (IMF) that had the biggest kurtosis index value, the resampled signal was decomposed into a series of IMFs; (3) the selected IMF was demodulated by means of HSD, and fault feature information could finally be obtained. The experimental results demonstrate the merit of the proposed method in gearbox fault diagnosis.
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paper_title: Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance

paper_content:

Abstract Characterized by small size, light weight and large transmission ratio, planetary gear transmission is widely used in large scale complex mechanical system with low speed and heavy duty. However, due to the influences of operating condition, manufacturing error, assembly error and multi-tooth meshing, the vibration signal of planetary gear exhibits the characteristics of nonlinear and non-stationary. Especially when early gear fault occurs, the weak fault feature information is submerged in interfering signal. A weak fault feature information extraction method of planetary gear based on Ensemble Empirical Mode Decomposition (EEMD) and Adaptive Stochastic Resonance (ASR) is proposed. The original signal is decomposed to the Intrinsic Mode Functions (IMFs) with small modal aliasing by EEMD. The Signal to Noise Ratio (SNR) of fault feature frequency information of each IMF is calculated, and the IMFs with first four higher SNR are reconstructed and selected as the effective IMFs containing main fault feature information. ASR system is built by combining Particle Swarm Optimization (PSO) and Stochastic Resonance (SR). PSO algorithm is used to optimize the critical parameters of SR, and SNR of ASR output signal is defined as an optimization objective. When the signal reconstructed by effective IMFs is inputted into ASR system, the weak fault feature information can be extracted from the output signal of ASR system. The experimental results show that the proposed method can extract the weak fault feature information of normal gear and fault gears successfully. The amplitudes of fault feature frequency and its sidebands generated by planetary gear fault have a significantly increase, and the effects on sideband amplitudes of faults become even greater than that on the amplitude of fault feature frequency. For different gear faults, the amplitude of fault feature frequency has different changes, meanwhile different sidebands are produced. Planetary gear fault diagnosis can be achieved accurately by comparing the extracted weak fault feature information, so it is an effective method of weak fault feature information extraction of planetary gear.
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paper_title: Early detection of tooth crack damage in gearbox using empirical wavelet transform combined by Hilbert transform

paper_content:

Hilbert-Huang Transform (HHT) has been renowned for its capacity to reveal fault indicating information issue from vibration signals. It uses Empirical Mode Decomposition (EMD) to decompose a signal accordingly to its contained information into a set of Intrinsic Mode Functions (IMFs). Then, the instantaneous frequencies are performed of each IMF using Hilbert Transform (HT). However, the HHT has some disadvantages which are caused by the EMD technique. The EMD has the mode mixing problem that may occur between IMFs, it causes the End Effect phenomenon, which leads to a wrong instantaneous values at both sides of the signal. Furthermore, its lack of mathematical basis. To overcome the HHT inherent problems, we propose the use of the Empirical Wavelet Transform (EWT) which designs an appropriate wavelet filter bank fully depends on the processed signal with HT in the early detection and condition monitoring of tooth crack fault. In this paper, we develop a dynamic model describing a single stage spur gear ...
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paper_title: Weak feature enhancement in machinery fault diagnosis using empirical wavelet transform and an improved adaptive bistable stochastic resonance

paper_content:

Abstract Machinery vibration signal is a typical multi-component signal and fault features are often submerged by some interference components. To accurately extract fault features, a weak feature enhancement method based on empirical wavelet transform (EWT) and an improved adaptive bistable stochastic resonance (IABSR) is proposed. This method makes full use of the signal decomposition performance of EWT and the signal enhancement of the IABSR to achieve the purpose of fault feature enhancement in low frequency band of FFT spectrum. Firstly, EWT is used as the preprocessing program of bistable stochastic resonance (BSR) to decompose the machinery vibration signal into a set of sub-components. Then, the sensitive component that contains main fault information is further input into BSR system to enhance fault features with the assistance of residual noises. Finally, the fault features are identified from fast Fourier transform (FFT) spectrum of the BSR output. To achieve the optimal BSR output, the IABSR method based on salp swarm algorithm (SSA) is presented. Compared with the tradition adaptive BSR (ABSR), the IABSR optimizes not only the BSR system parameters but also the calculation step size. Two case studies on machinery fault diagnosis demonstrate the effectiveness and superiority of the proposed method. In addition, the proposed method is easy to implement and is robust to noise to some extent.
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paper_title: Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals

paper_content:

The implementation of condition monitoring and fault diagnosis system (CMFDS) on wind turbine is significant to lower the unscheduled breakdown. Generator is one of the most important components in wind turbine, and generator bearing fault identification always draws lots of attention. However, non-stationary vibration signal of weak fault and compound fault with a large amount of background noise makes this task challenging in many cases. So, effective signal processing method is essential in the accurate diagnosis step of CMFDS. As a novel signal processing method, empirical Wavelet Transform (EWT) is used to extract inherent modulation information by decomposing signal into mono-components under an orthogonal basis, which is seen as a powerful tool for mechanical fault diagnosis. Moreover, in order to avoid the inaccurate identification the internal modes caused by the heavy noise, wavelet spatial neighboring coefficient denoising with data-driven threshold is applied to increase Signal to Noise Ratio (SNR) before EWT. The effectiveness of the proposed technique on weak fault and compound fault diagnosis is first validated by two experimental cases. Finally, the proposed method has been applied to identify fault feature of generator bearing on wind turbine in wind farm successfully.
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paper_title: Detection of weak fault using sparse empirical wavelet transform for cyclic fault

paper_content:

The successful prediction of the remaining useful life of rolling element bearings depends on the capability of early fault detection. A critical step in fault diagnosis is to use the correct signal processing techniques to extract the fault signal. This paper proposes a newly developed diagnostic model using a sparse-based empirical wavelet transform (EWT) to enhance the fault signal to noise ratio. The unprocessed signal is first analyzed using the kurtogram to locate the fault frequency band and filter out the system noise. Then, the preprocessed signal is filtered using the EWT. The lq-regularized sparse regression is implemented to obtain a sparse solution of the defect signal in the frequency domain. The proposed method demonstrates a significant improvement of the signal to noise ratio and is applicable for detection of cyclic fault, which includes the extraction of the fault signatures of bearings and gearboxes.
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paper_title: A multi-order FRFT self-adaptive filter based on segmental frequency fitting and early fault diagnosis in gears

paper_content:

Abstract To effectively diagnose gear failure at an early stage, a multi-order Fractional Fourier transform (FRFT) self-adaptive filter based on segmental frequency fitting (MFSFF) is proposed to separate the feature components with curved frequency from the gearbox’s transient conditions. First, a linear multi-scale segmentation method (LMSS) is developed to divide the signal with curved frequency into segments with nearly linear frequency; then, a method for determining the FRFT filter parameters by fitting the frequency curve (DFFPFF) is developed to calculate the FRFT filter parameters for each signal segment, and the signal in each segment is filtered by an FRFT filter using these parameters to determine the MFSFF. The vibration of the gearbox’s acceleration and deceleration process is analyzed using an MFSFF and the filtered signal is demodulated. The experimental results show that LMSS is able to divide any signal with curved frequency into minimal segments with nearly linear frequency; DFFPFF is exact, fast, not influenced by the vibration source or the number of components, and able to determine the FRFT filter parameters for each signal segment accurately; the feature component of the gearbox’s transient condition is accurately extracted by an MFSFF, and the other components and noise are removed simultaneously. Early gear failure is diagnosed exactly by demodulation of the extracted feature component, which is difficult to identify using the traditional method.
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paper_title: Effectiveness of Adaptive Filter Algorithms and Spectral Kurtosis in Bearing Faults Detection in a Gearbox

paper_content:

Bearing faults detection at the earliest stages is vital in avoiding future catastrophic failures. Many traditional techniques have been established and utilized in detecting bearing faults, though, these diagnostic techniques are not always successful when the bearing faults take place in gearboxes where the vibration signal is complex; under such circumstances it may be necessary to separate the bearing signal from the complex signal. The objective of this paper is to assess the effectiveness of an adaptive filter algorithms compared to a Spectral Kurtosis (SK) algorithm in diagnosing a bearing defects in a gearbox. Two adaptive filters have been used for the purpose of bearing signal separation, these algorithms were Least Mean Square (LMS) and Fast Block LMS (FBLMS) algorithms. These algorithms were applied to identify a bearing defects in a gearbox employed for an aircraft control system for which endurance tests were performed. The results show that the LMS algorithm is capable of detecting the bearing fault earlier in comparison to the other algorithms.
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paper_title: Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System

paper_content:

Reliable monitoring for the early fault diagnosis of gearbox faults is of great concern for the wind industry. This paper presents a novel approach for health condition monitoring (CM) and fault diagnosis in wind turbine gearboxes using vibration analysis. This methodology is based on a machine learning algorithm that generates a baseline for the identification of deviations from the normal operation conditions of the turbine and the intrinsic characteristic-scale decomposition (ICD) method for fault type recognition. Outliers picked up during the baseline stage are decomposed by the ICD method to obtain the product components which reveal the fault information. The new methodology proposed for gear and bearing defect identification was validated by laboratory and field trials, comparing well with the methods reviewed in the literature.
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paper_title: A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery

paper_content:

Abstract Singular value decomposition (SVD), as an effective signal denoising tool, has been attracting considerable attention in recent years. The basic idea behind SVD denoising is to preserve the singular components (SCs) with significant singular values. However, it is shown that the singular values mainly reflect the energy of decomposed SCs, therefore traditional SVD denoising approaches are essentially energy-based, which tend to highlight the high-energy regular components in the measured signal, while ignoring the weak feature caused by early fault. To overcome this issue, a reweighted singular value decomposition (RSVD) strategy is proposed for signal denoising and weak feature enhancement. In this work, a novel information index called periodic modulation intensity is introduced to quantify the diagnostic information in a mechanical signal. With this index, the decomposed SCs can be evaluated and sorted according to their information levels, rather than energy. Based on that, a truncated linear weighting function is proposed to control the contribution of each SC in the reconstruction of the denoised signal. In this way, some weak but informative SCs could be highlighted effectively. The advantages of RSVD over traditional approaches are demonstrated by both simulated signals and real vibration/acoustic data from a two-stage gearbox as well as train bearings. The results demonstrate that the proposed method can successfully extract the weak fault feature even in the presence of heavy noise and ambient interferences.
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paper_title: Adaptive filtering based system for extracting gearbox condition feature from the measured vibrations

paper_content:

Vibration signals measured from a gearbox are complex multi-component signals, generated by tooth meshing, gear shaft rotation, gearbox resonance vibration signatures and a substantial amount of noise. This article presents a novel scheme for extracting gearbox fault features using adaptive filtering techniques for enhancing condition features, meshing frequency sidebands. A modified least mean square (LMS) algorithm is developed and validated using only one accelerometer, instead of using two accelerometers in traditional arrangement, as the main signal and a desired signal is artificially generated from the measured shaft speed and gear meshing frequencies. The proposed scheme is applied to a signal simulated from gearbox frequencies with a numerous values of step size. Findings confirm that 10−5 step size invariably produces more accurate results and there has been a substantial improvement in signal clarity (better signal-to-noise ratio); which make meshing frequency sidebands more discernible. The developed scheme is validated via a number of experiments carried out using two-stage helical gearbox for a pair of healthy gears and one pair suffering from a tooth breakage with severity fault 1 (25% tooth removal), and fault 2 (50% tooth removal) under loads (0%, and 80% of the total load). The experimental results show remarkable improvements and enhance gear fault features. This paper illustrates that the new approach offers a more effective way to detect early faults.
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paper_title: Health monitoring of cooling fan bearings based on wavelet filter

paper_content:

Abstract In this paper, a vibration-based health monitoring approach for cooling fans is proposed using a wavelet filter for early detection of faults in fan bearings and for the assessment of fault severity. To match the wavelet filter to the fault characteristic signal, a fuzzy rule is introduced to maximize the amplitudes of bearing characteristic frequencies (BCFs), which are an indicator of bearing faults. The sum of the amplitudes of BCFs and their harmonics (SABCF) is used as an index to capture the bearing degradation trend. A comparative study is conducted with commonly used time-domain indices in the degradation assessment, and performance is quantified by three measures, i.e., monotonicity, prognosability, and trendability. The analysis results of the experimental data show that the proposed method can effectively detect incipient defects and can better capture the degradation trend of fan bearings than traditional time-domain indices in vibration analysis.
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paper_title: Maximal-overlap adaptive multiwavelet for detecting transient vibration responses from dynastic signal of rotating machineries

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Vibration signal is an important prerequisite for mechanical fault detection. However, early stage defect of rotating machineries is difficult to identify because their incipient energy is interfered with background noises. Multiwavelet is a powerful tool used to conduct non-stationary fault feature extraction. However, the existing predetermined multiwavelet bases are independent of the dynamic response signals. In this paper, a constructing technique of vibration data-driven maximal-overlap adaptive multiwavelet (MOAMW) is proposed for enhancing the extracting performance of fault symptom. It is able to derive an optimal multiwavelet basis that best matches the critical non-stationary and transient fault signatures via genetic algorithm. In this technique, two-scale similarity transform (TST) and symmetric lifting (SymLift) scheme are combined to gain high designing freedom for matching the critical faulty vibration contents in vibration signals based on the maximal fitness objective. TST and SymLift can add modifications to the initial multiwavelet by changing the approximation order and vanishing moment of multiwavelet, respectively. Moreover, the beneficial feature of the MOAWM lies in that the maximal-overlap filterbank structure can enhance the periodic and transient characteristics of the sensor signals and preserve the time and frequency analyzing resolution during the decomposition process. The effectiveness of the proposed technique is validated via a numerical simulation as well as a rolling element bearing with an outer race scrape and a gearbox with rub fault.
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paper_title: A joint sparse wavelet coefficient extraction and adaptive noise reduction method in recovery of weak bearing fault features from a multi-component signal mixture

paper_content:

Rolling element bearings are widely used to support rotating components of a machine. Due to close space locations of components in the machine, a vibration signal caused by bearing localized defects is easily overwhelmed by other strong vibration signals. Extracting the bearing fault signal from a multi-component signal mixture is thus significant to detect early bearing fault features and prevent machine breakdown. In this paper, a bearing fault diagnosis method, named cyclic spike detection method, is proposed to extract the weak bearing fault features from a multi-component signal mixture. Firstly, the optimal center frequency and bandwidth of a complex Morlet wavelet filter are determined by a simplex-simulated annealing algorithm along with a maximum sparsity objective function. The filtered signal is then obtained by applying the optimal wavelet filter to the multi-component signal mixture. After that, a new adaptive local maximum selection method is proposed to make the filtered signal succinct. Only a few spikes are retained to reveal potential cyclic intervals caused by bearing localized defects. Two multi-component signal mixtures, including a simulated signal and a real vibration signal collected from an industrial machine, are used to validate the effectiveness of the proposed cyclic spike detection method. The results demonstrate that the proposed method can extract the weak bearing fault features from other strong masking vibration signals and noise.
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paper_title: Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform

paper_content:

Abstract When a fault occurs on bearings, the measured bearing fault signals contain both high Q-factor oscillation component and low Q-factor periodic impact component. TQWT is the improvement of the traditional single Q-factor wavelet transform, which is very suitable for separating the low Q-factor component from the high Q-factor component. However, the accuracy of its decomposition heavily depended on the selection of Q-factors. There is no reported simple but effective method to select the Q-factors with enough accuracy. This study aims to develop a strategy to diagnostic the early fault of rolling bearings. In this paper, a characteristic frequency ratio (CFR) is used to optimize Q-factors of TQWT (OTQWT). However, directly application of OTQWT is difficult to extract fault signatures at early stage due to the weak fault symptoms and strong noise. A strategy of combination of intrinsic characteristic-scale decomposition (ICD) and TQWT is proposed. ICD owns significant advantages on computation efficiency and alleviation of mode mixing. The effectiveness of the proposed strategy is tested with both simulated and experimental vibration signals. Meanwhile, comparisons are conducted between the proposed method and other methods like: envelope demodulation and EEMD-TQWT. Results show that the proposed method has superior performance in extracting fault features of defective bearings at an early stage.
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paper_title: A novel adaptive wavelet stripping algorithm for extracting the transients caused by bearing localized faults

paper_content:

Rolling element bearings are widely used in rotating machinery. Its unexpected failure may result in machine breakdown. Whenever a bearing suffers a localized fault, the transients with a potential cyclic characteristic are generated by the rollers striking the localized fault. This phenomenon is an early bearing fault feature. Therefore, the extraction of the transients is beneficial to the identification of the early bearing fault. In this paper, a novel adaptive wavelet stripping algorithm (AWSA) is proposed to extract the simulated transients from an original bearing fault signal. Firstly, the parametric model of anti-symmetric real Laplace wavelet (ARLW) or impulse response wavelet (IRW) is built to approximate the real transients. Then, with the aid of wavelet correlation filtering analysis, the simulated transients with the optimal frequency, damping coefficient and delay time are adaptively peeled from the original bearing fault signal. The spatial reconstruction of the simulated transients reflects the random occurrence of the real transients. In order to boost the computing time of the AWSA, an enhanced AWSA is developed. At last, the bearing fault signals collected from an experimental machine and an industrial machine are used to validate the effectiveness of the AWSA. The results show that the AWSA can adaptively peel the simulated transients from the original bearing fault signals. A comparison with a periodic multi-transient model is conducted to show that the AWSA is better to extract the random characteristics of the real transients.
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paper_title: Novel detection of local tooth damage in gears by the wavelet bicoherence

paper_content:

Abstract A new technique, the instantaneous wavelet bicoherence (WB) is proposed and investigated. The use of the instantaneous and locally averaged WB from vibration measurements for local damage detection in gears is investigated for the first time; these bicoherences are better adapted than the classical Fourier bicoherence to the case of non-stationary signals. A new diagnostic feature based on the integrated modulus of the WB in a specific frequency range and a methodology for feature estimation are proposed. The WB techniques are applied to detection of a multiple “like natural” pitting on a back-to-back industrial spur gearbox system and natural pitting on a gear at test rig and show the possibility of early detection of local tooth faults. The detection effectiveness is evaluated by a local Fisher criterion estimated at each angular position of gear for the unpitted and pitted cases. The proposed WB-based diagnostic feature demonstrates robust experimental performance and superior detection capabilities (i.e., effective early damage detection differentially for teeth of the gear wheel) over the conventional detection methods based on the wavelet transform. The reason for this superior effectiveness is that the WB exploits the phase couplings of the wavelet transform at different frequencies, which contain useful additional information for detection of non-linear phenomena induced by local faults. The proposed approaches are compared with the two conventional approaches based on the wavelet transform.
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paper_title: Industrial drive fault diagnosis through vibration analysis using wavelet transform

paper_content:

The fault diagnosis of electrical machines is significant to reduce the costs of maintenance through early detection of faults, which could be expensive to service. The idea of the research is that the advanced signal processing techniques called wavelet transform is exercised to haul out the faults from the vibration and current signal and a spectral component is being received to diagnose the condition of the machine. The existing system used wavelet transforms for the analysis of the stator faults and rotor faults are taken as a case study to prove the wavelet techniques for fault diagnosis. The proposed investigation of vibration analysis for the induction machines is done through frequency pattern using the decomposition of wavelet packet. The wavelet coefficients for the vibration have been extracted over a wide range of signals and the analysis could be percolated on the frequency domain through HAAR wavelet. In this paper, healthy and unhealthy motor with two broken ball bearings are used to estim...
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paper_title: An investigation of rolling bearing early diagnosis based on high-frequency characteristics and self-adaptive wavelet de-noising

paper_content:

Rolling bearings are necessary parts in rotary machines. However, the problem of early fault diagnosis for rolling bearings is difficult to solve due to its low signal-to-noise ratio and non-linear and non-stationary signal. Based on a detailed investigation of rolling bearing vibration signals, this paper proposes a method for determining whether a fault occurs by comparing the high-frequency band power. If a fault occurs, we first de-noise the vibration signals using wavelet de-noising and then extract the fault characteristics in both the time domain and the time-frequency domain to avoid the limitations of using only one domain. Finally, the fault location is identified using the grey correlation method. According to the method application results, the recognition accuracy using the method proposed in this paper is satisfactory, proving that the method has superior performance.
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paper_title: Construction of adaptive redundant multiwavelet packet and its application to compound faults detection of rotating machinery

paper_content:

It is significant to detect the fault type and assess the fault level as early as possible for avoiding catastrophic accidents. Due to diversity and complexity, the compound faults detection of rotating machinery under non-stationary operation turns to be a challenging task. Multiwavelet with two or more base functions may match two or more features of compound faults, which may supply a possible solution to compound faults detection. However, the fixed basis functions of multiwavelet transform, which are not related with the vibration signal, may reduce the accuracy of compound faults detection. Moreover, the decomposition results of multiwavelet transform not being own time-invariant is harmful to extract the features of periodical impulses. Furthermore, multiwavelet transform only focuses on the multi-resolution analysis in the low frequency band, and may leave out the useful features of compound faults. To overcome these shortcomings, a novel method called adaptive redundant multiwavelet packet (ARMP) is proposed based on the two-scale similarity transforms. Besides, the relative energy ratio at the characteristic frequency of the concerned component is computed to select the sensitive frequency bands of multiwavelet packet coefficients. The proposed method was used to analyze the compound faults of rolling element bearing. The results showed that the proposed method could enhance the ability of compound faults detection of rotating machinery.
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paper_title: Application of the wavelets multiresolution analysis and the high-frequency resonance technique for gears and bearings faults diagnosis

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Defects diagnosis and condition surveillance of production and manufacturing rotating machinery in a plant is very important for guaranteeing production efficiency and plant safety. Condition surveillance for gear and bearing defects diagnosis for all rotating machines is a serious job because they cause accidents and consequently great production losses. For gear and bearing faults, and early detection especially in the gearboxes, researchers in the conditional maintenance and vibratory analysis used different methods and techniques in signal processing, among those and in full rise, demodulation by wavelets multiresolution analysis (WMRA) and high-frequency resonance technique (HFRT), based on the Hilbert transform, which allows filtering and the demodulation at the same time. In this paper, we propose to make a precise diagnosis for gears and bearings combined faults detection and identification in a laboratory test rig which simulate a rotating machine like in the manufacturing processes using WMRA and HFRT techniques. First of all, we applied WMRA method on simulated signals of gear or bearing defects or the combination of them, then we applied it on real signals measured on a test rig of the LMS laboratory in the University of Guelma.
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paper_title: A Wavelet-Based Statistical Approach for Monitoring and Diagnosis of Compound Faults With Application to Rolling Bearings

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This paper proposes a wavelet-based statistical signal detection approach for monitoring and diagnosis of bearing compound faults at an early stage. The bearing vibration signal is decomposed by an orthonormal discrete wavelet transform to obtain its energy dispersions at multiple levels. We investigate the statistical properties of the decomposed signal energy under both the normal and faulty conditions, based on which a generalized likelihood ratio test is developed. An exponentially weighted moving average control chart is then constructed to detect faults at an early stage. Simulation studies and a real case study are conducted to demonstrate the effectiveness of the proposed method. Furthermore, the comparison studies show that the proposed method outperforms the empirical mode decomposition method and Hilbert envelope spectrum analysis method. Note to Practitioners —This paper is motivated by the problem of monitoring and diagnosis of compound faults in rolling bearings at the early stage, which are seldom considered in existing methods. In this paper, we propose a new approach by using statistical signal detection method and wavelet transform to handle the fault signals. This work aims at monitoring vibration signals and diagnosing fault types. Our simulation results show the proposed approach outperforms existing methods, especially at an early stage. Our future work will aim at improving the method’s sensitivity in distinguishing faults similar to each other.
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paper_title: Online unbalanced rotor fault detection of an IM drive based on both time and frequency domain analyses

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Effective maintenance program provides incipient fault detection of rotating machines which reduces interim, unscheduled and excessive maintenance actions. By applying suitable online condition monitoring accompanied with signal processing techniques, machines irregularity can be detected at early stage. Therefore, this paper presents an online unbalanced rotor fault detection of an induction motor (IM). Characteristic features of motor current and vibration signals are analyzed in time domain as a fault diagnosis technique which is a key parameter to the fault threshold. Motor current and vibration signal analyses are also done based on Fast Fourier Transform (FFT), Hilbert Transform (HT) and Envelope Detection (ED) with low pass filter method to detect the severity of the fault and its possible location under different load conditions. The effectiveness of the electrical and mechanical fault detections based on FFT, HT and ED analyses are verified using the captured experimental data while the motor is running under different load conditions. The magnitudes of the spectral components are extracted for the pattern reorganization of the fault. Techniques are used under normal and unbalanced rotor conditions to a 3-phase, 2 pole, 0.246 KW, 60 Hz, 2950 rpm IM drive.
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paper_title: Vibration condition monitoring system for wind turbine bearings based on noise suppression with multi-point data fusion

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A noise suppression method for feature frequency extraction that is supplemented with multi-point data fusion was investigated in consideration of issues involving wind turbine vibration signals subject to high noise disturbance. The difficulty of extracting early weak fault features was examined as well. First, a de-noising and feature extraction method that uses EMD-Correlation was developed by adding empirical mode decomposition (EMD) and autocorrelation de-noising to the wavelet package transform under the effects of white noise and short-term disturbance noise in wind turbine vibration signals. Second, an EMD-Correlation analysis model for feature frequency extraction supplemented with multi-point data fusion was established with reference to adaptive resonance theory-2 to highlight the feature frequency of a possible early weak fault. Third, the results obtained with the actual and simulated fault vibration signals of wind turbine bearing faults and the outcomes of comparing the different feature frequency extraction methods show that the proposed method of EMD-Correlation that is supplemented with multi-point data fusion can not only effectively reduce white noise and short-term disturbance noise but can also extract the feature frequency of early weak faults. Finally, prototype hardware and software were developed for a wind turbine condition monitoring system based on the aforementioned fault feature extraction algorithms and tested in an actual wind turbine generation system.
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paper_title: Enhanced Orthogonal Matching Pursuit Algorithm and Its Application in Mechanical Equipment Fault Diagnosis

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The vibration signal measured from the mechanical equipment is associated with the operation of key structure, such as the rolling bearing and gear. The effective signal processing method for early weak fault has attracted much attention and it is of vital importance in mechanical fault monitoring and diagnosis. The recently proposed atomic sparse decomposition algorithm is performed around overcomplete dictionary instead of the traditional signal analysis method using orthogonal basis operator. This algorithm has been proved to be effective in extracting useful components from complex signal by reducing influence of background noises. In this paper, an improved linear frequency-modulated (Ilfm) function as an atom is employed in the proposed enhanced orthogonal matching pursuit (EOMP) algorithm. Then, quantum genetic algorithm (QGA) with the OMP algorithm is integrated since the QGA can quickly obtain the global optimal solution of multiple parameters for rapidly and accurately extracting fault characteristic information from the vibration signal. The proposed method in this paper is superior to the traditional OMP algorithm in terms of accuracy and reducing the computation time through analyzing the simulation data and real world data. The experimental results based on the application of gear and bearing fault diagnosis indicate that it is more effective than traditional method in extracting fault characteristic information.
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paper_title: Sparse classification of rotating machinery faults based on compressive sensing strategy

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Abstract Rotating machinery is integral to production processes and is composed of several components susceptible to malfunction. Safety and stable operation necessitate early identification of potential faults. Fault identification and classifications for rotating machinery are investigated in this study utilizing expanded monitoring data. A representation classification strategy for rotating machinery faults is developed based on a newly developed compressive sensing theory focusing on extraction and classification of fault features through sparse representation combined with random dimensionality reduction mapping. Original characteristics of a vibration signal are sampled and preserved by applying a small number of random projections and a learning redundant dictionary then constructed to sparsely represent the vibration signal. Fault signal impulse information is determined through an optimization strategy with sparsity promoting. A rolling bearing is utilized as an example in the study with simulations and experiments indicating that fault characteristics may be directly extracted from a small number of random projections without ever reconstructing the vibration signal completely and with minimal prior fault characteristic frequency knowledge required.
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paper_title: The Delayed Correlation Envelope Analysis Technique Based on Sparse Signal Decomposition Method and its Application to Bearing early Fault Diagnosis

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In order to extract the early weak fault information submerged in strong background noise of the bearing vibration signal, a delayed correlation envelope technique based on sparse signal decomposition method is proposed. This method can improve the signal to noise and extract the fault information efficiently. For the strong noise problem in the early fault, based on D-value of adjacent residual energy as the termination condition of the iterative method, reducing the noise effectively, and combine it with delayed correlation to enhance the denoising effect. The analysis results of roller bearing experimental data confirm the feasibility and validity of this method.
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paper_title: Sparse representation based latent components analysis for machinery weak fault detection

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Abstract Weak machinery fault detection is a difficult task because of two main reasons (1) At the early stage of fault development, signature of fault related component performs incompletely and is quite different from that at the apparent failure stage. In most instances, it seems almost identical with the normal operating state. (2) The fault feature is always submerged and distorted by relatively strong background noise and macro-structural vibrations even if the fault component already performs completely, especially when the structure of fault components and interference are close. To solve these problems, we should penetrate into the underlying structure of the signal. Sparse representation provides a class of algorithms for finding succinct representations of signal that capture higher-level features in the data. With the purpose of extracting incomplete or seriously overwhelmed fault components, a sparse representation based latent components decomposition method is proposed in this paper. As a special case of sparse representation, shift-invariant sparse coding algorithm provides an effective basis functions learning scheme for capturing the underlying structure of machinery fault signal by iteratively solving two convex optimization problems: an L1-regularized least squares problem and an L2-constrained least squares problem. Among these basis functions, fault feature can be probably contained and extracted if optimal latent component is filtered. The proposed scheme is applied to analyze vibration signals of both rolling bearings and gears. Experiment of accelerated lifetime test of bearings validates the proposed method׳s ability of detecting early fault. Besides, experiments of fault bearings and gears with heavy noise and interference show the approach can effectively distinguish subtle differences between defect and interference. All the experimental data are analyzed by wavelet shrinkage and basis pursuit de-noising (BPDN) method for comparison.
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paper_title: Fault diagnosis of rolling bearing’s early weak fault based on minimum entropy de-convolution and fast Kurtogram algorithm

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The rolling bearing’s early stage fault feature is very weak for reasons of the signal attenuation phenomenon between the fault source and the sensor collecting the fault signal and the interference of environment noise such as the rotor rotating frequency and its harmonics and so on. The feature extraction of rolling bearing’s early weak fault is not only very important but also very hard. The minimum entropy de-convolution and Fast Kurtogram algorithm are combined in the paper for rolling bearing’s early stage weak fault feature extraction. The effect of transmission path is de-convolved effectively, and the impulses are clarified using minimum entropy de-convolution technique firstly. Then the obtained signal by minimum entropy de-convolution is handled by the Fast Kurtogram algorithm and an optimal filter is established. At last the envelope de-modulation is applied on the filtered signal and better feature extraction result is obtained compared with the other methods such as wavelet transform, freque...
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paper_title: Fast computation of the spectral correlation

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Although the Spectral Correlation is one of the most versatile spectral tools to analyze cyclostationary signals (i.e. signals comprising hidden periodicities or repetitive patterns), its use in condition monitoring has so far been hindered by its high computational cost. The Cyclic Modulation Spectrum (the Fourier transform of the spectrogram) stands as a much faster alternative, yet it suffers from the uncertainty principle and is thus limited to detect relatively slow periodic modulations. This paper fixes the situation by proposing a new fast estimator of the spectral correlation, the Fast Spectral Correlation, based on the short-time Fourier transform (STFT). It proceeds from the property that, for a cyclostationary signal, the STFT evidences periodic flows of energy in and across its frequency bins. The Fourier transform of the interactions of the STFT coefficients then returns a quantity which scans the Spectral Correlation along its cyclic frequency axis. The gain in computational cost as compared to the conventional estimator is like the ratio of the signal length to the STFT window length and can therefore be considerable. The validity of the proposed estimator is demonstrated on non trivial vibration signals (very weak bearing signatures and speed varying cases) and its computational advantage is used to compute a new quantity, the Enhanced Envelope Spectrum.
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paper_title: Noise resistant time frequency analysis and application in fault diagnosis of rolling element bearings

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Abstract Rolling element bearings are frequently used in rotary machinery, but they are also fragile mechanical parts. Hence, exact condition monitoring and fault diagnosis for them plays an important role in ensuring machinery's reliable running. Timely diagnosis of early bearing faults is desirable, but the early fault signatures are easily submerged in noise. In this paper, Wigner–Ville spectrum based on cyclic spectral density (CSWVS for a brief notation) is studied, which is able to represent the cyclostationary signals while reducing the masking effect of additive stationary noise. Both simulations and experiments show that CSWVS is a noise resistant time frequency analysis technique for extracting bearing fault patterns, when bearing signals are under influences of random noise and gear vibrations. The 3-D feature of the CSWVS is proved useful in extracting bearing fault pattern from gearbox vibration signals, where bearing signals are affected by gear meshing vibration and noise. Besides, CSWVS utilizes the second order cyclostationary property of the vibration signals produced by bearing distributed fault, and clearly extracts its fault features, which cannot be extracted by envelope analysis. To quantitatively describe the extent of bearing fault, Renyi information encoded in the time frequency diagram of CSWVS is studied. It is shown to be a more sensitive index to reflect bearing performance degradation, compared with the spectral entropy (SE), squared envelope spectrum entropy (SESE) and Renyi informations for WVD, PWVD, especially when SNR is low.
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paper_title: Improving the weak feature extraction by adaptive stochastic resonance in cascaded piecewise-linear system and its application in bearing fault detection

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In mechanical engineering field, early fault features are extremely weak and submerged in heavy noise, and the weak feature extraction is quite challenging. In this work, we apply the adaptive stochastic resonance in cascaded piecewise-linear system to extract the weak features. The adaptive stochastic resonance is realized by the quantum particle swarm algorithm. By optimizing system parameters, the efficiency of the feature extraction is improved greatly. As a result, the weak features can be easily extracted eventually. The effectiveness and the high-performance of the proposed method are verified by the numerical simulation and experimental data of rolling element bearings. The bearing fault under different motor loads is detected effectively, consequently confirming the robustness of the proposed method.
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paper_title: Nonlinear Parameters for Monitoring Gear: Comparison Between Lempel-Ziv, Approximate Entropy, and Sample Entropy Complexity

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Vibration analysis is the most used technique for defect monitoring failures of industrial gearboxes. Detection and diagnosis of gear defects are thus crucial to avoid catastrophic failures. It is therefore important to detect early fault symptoms. This paper introduces signal processing methods based on approximate entropy (ApEn), sample entropy (SampEn), and Lempel-Ziv Complexity (LZC) for detection of gears defects. These methods are based on statistical measurements exploring the regularity of vibratory signals. Applied to gear signals, the parameter selection of ApEn, SampEn, and LZC calculation is first numerically investigated, and appropriate parameters are suggested. Finally, an experimental study is presented to investigate the effectiveness of these indicators and a comparative study with traditional time domain indicators is presented. The results demonstrate that ApEn, SampEn, and LZC provide alternative features for signal processing. A new methodology is presented combining both Kurtosis and LZC for early detection of faults. The results show that this proposed method may be used as an effective tool for early detection of gear faults.
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paper_title: Fast computation of the kurtogram for the detection of transient faults

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Abstract The kurtogram is a fourth-order spectral analysis tool recently introduced for detecting and characterising non-stationarities in a signal. The paradigm relies on the assertion that each type of transient is associated with an optimal (frequency/frequency resolution) dyad { f , Δ f } which maximises its kurtosis, and hence its detection. However, the complete exploration of the whole plane ( f , Δ f ) is a formidable task hardly amenable to on-line industrial applications. In this communication we describe a fast algorithm for computing the kurtogram over a grid that finely samples the ( f , Δ f ) plane. Its complexity is on the order of N log N , similarly to the FFT. The efficiency of the algorithm is then illustrated on several industrial cases concerned with the detection of incipient transient faults.
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paper_title: Fuzzy determination of informative frequency band for bearing fault detection

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Detecting early faults in rolling element bearings is a crucial measure for the health maintenance of rotating machinery. As faulty features of bearings are usually demodulated into a high-frequency band, determining the informative frequency band (IFB) from the vibratory signal is a challenging task for weak fault detection. Existing approaches for IFB determination often divide the frequency spectrum of the signal into even partitions, one of which is regarded as the IFB by an individual selector. This work proposes a fuzzy technique to select the IFB with improvements in two aspects. On the one hand, an IFB-specific fuzzy clustering method is developed to segment the frequency spectrum into meaningful sub-bands. Considering the shortcomings of the individual selectors, on the other hand, three commonly-used selectors are combined using a fuzzy comprehensive evaluation method to guide the clustering. Among all the meaningful sub-bands, the one with the minimum comprehensive cost is determined as the IFB. The bearing faults, if any, can be detected from the demodulated envelope spectrum of the IFB. The proposed fuzzy technique was evaluated using both simulated and experimental data, and then compared with the state-of-the-art peer method. The results indicate that the proposed fuzzy technique is capable of generating a better IFB, and is suitable for detecting bearing faults.
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paper_title: Early fault detection in automotive ball bearings using the minimum variance cepstrum

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Abstract Ball bearings in automotive wheels play an important role in a vehicle. They enable an automobile to run and simultaneously support the vehicle. Once faults are generated, even if they are small, they often grow fast even under normal driving condition and cause vibration and noise. Therefore, it is critical to detect faults as early as possible to prevent bearings from generating harsh noise and vibration. How early faults can be detected is associated with how well a detecting method finds the information of early faults from measured signal. Incipient faults are so small that the fault signal is inherently buried by noise. Minimum variance cepstrum (MVC) has been introduced for the observation of periodic impulse signal under noisy environments. We are particularly focusing on the definition of MVC that goes back to the original definition by Bogert et al. in comparison with the recently prevalent definition of cepstral analysis. In this work, the MVC is, therefore, obtained by liftering a logarithmic power spectrum, and the lifter bank is designed by the minimum variance algorithm. Furthermore, it is also shown how efficient the method is for detecting periodic fault signal made by early faults by using automotive ball bearings, with which an automobile is equipped under running conditions. We were able to detect incipient faults in 4 out of 12 normal bearings which passed acceptance test as well as in bearings that were recalled due to noise and vibration. In addition, we compared the results of the proposed method with results obtained using other older well-established early fault detection methods that were chosen from 4 groups of methods which were classified by the domain of observation. The results demonstrated that MVC determined bearing fault periods more clearly than other methods under the given condition.
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paper_title: Periodically correlated random processes: Application in early diagnostics of mechanical systems

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Abstract The covariance and spectral characteristics of periodically correlated random processes (PCRP) are used to describe the state of rotary mechanical systems and in their fault detection. The methods for estimation of mean function, covariance function, instantaneous spectral density and their Fourier coefficients for a given class of non-stationary random processes on the basis of experimental data, namely: the synchronous averaging, component, least squares method and linear filtration methods are considered. The first and second order periodicity detection methods are used for vibration signals analysis. A method for mechanical system fault identification and classification based on a harmonic series representation is developed. Examples of fault detection in rolling/sliding bearings and gearboxes are given.
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paper_title: A frequency-shifted bispectrum for rolling element bearing diagnosis

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Abstract This paper is in the field of vibration based fault diagnosis for rolling element bearings, where one major issue is the ability to detect faults as early as possible. Bispectrum analysis has been applied in bearing diagnosis. In this paper, a frequency-shifted bispectrum is studied for bearing diagnosis. It is found that the frequency-shifted bispectrum is more suitable for analyzing amplitude modulated and frequency modulated signals than the bispectrum, when choosing the carrier frequency as the specific frequency shift value. For bearing vibration signals with amplitude modulation characteristics, in which the carrier frequencies are structural resonant frequencies, one of the structural resonances can be chosen as the specific frequency shift value for the frequency-shifted bispectrum analysis. To increase diagnostic ability of the frequency-shifted bispectrum for bearing faults, firstly, the frequency resolution is increased in the frequency-shifted bispectrum analysis through band pass filtering and frequency zooming; secondly, integration of the frequency-shifted bispectrum along one frequency variable is used to produce a one-dimensional spectral representation. Numerical simulation and experimental results show the feasibility of the proposed method for qualitatively and quantitatively diagnosing bearing faults.
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paper_title: An Automatic Filtering Method Based on an Improved Genetic Algorithm—With Application to Rolling Bearing Fault Signal Extraction

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Fault feature extracted from fault signals plays a vital role in fault detection and diagnosis. However, in the early stage of defect, the fault signals (amplitudes and energy) are very weak and overwhelmed by large noise, and it is very difficult to extract fault feature effectively. To handle this problem, in this paper, an automatic filtering method based on an improved genetic algorithm (IGA) for extracting fault signals is presented. This method improves the binary coding generation of GA, so that each binary value matches a frequency of a fault signal frequency spectrum. Based on designed “statistical information evaluation function” as IGA fitness function, the similarity between normal state signal (noise signal) and IGA extracted noise signal is evaluated, and IGA employs genetic operations to search the optimal noise binary string. Negating noise binary string can obtain optimal fault signal binary string. It will then be multiplied by abnormal state vibration signal frequency spectrum, to yield a new frequency spectrum that only has fault components. This proposed method does not need to seek center frequency and bandwidth, and can automatically remove noise. The effective and advanced performance of the proposed method is evaluated by three different rolling bearing defects (outer race defect, inner race defect, and roller defect) in rolling bearing on rotating machine experimental bench and gearbox. Compared with rolling bearing fault characteristic frequency and results of denoising methods of bandpass filtering and wavelet decomposition, the proposed method can automatically filter noise and effectively extract fault signal. It proves that this method is feasible, effective, and advanced.
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paper_title: Incipient feature extraction for rolling element bearing based on particle filter preprocessing and kurtogram

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Fast kurtogram (FK) has been broadly investigated on fault diagnosis for rolling element bearing(REB) since it is put forward. But its performance is low as incipient feature extraction and fault diagnosis of REB as noise has greatly effect on the classification accuracy. How to effectively improve the signal-to-noise ratio(SNR) is important for characteristic frequency(CF) determination based on FK. In this research, the SNR can be improved by using particle filter(PF) to be satisfied with noise interference. It can be useful for the improvement of FK after de-noise. Firstly, state space function for the analyzing signal is constructed, background noise of origin signal can be extracted by using the PF and improve the SNR. Then, the optimal band is chosen by using FK after PF preprocessing. In the end, fault CF can be obtained by using spectrum analysis. Simulation and monitored vibration signals for REB are used to verify the effectiveness of this method. It can be concluded that the proposed method has better performance compared with that of FK and EMD-Kurtogram on REB fault early classification.
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paper_title: A new fault diagnosis algorithm for helical gears rotating at low speed using an optical encoder

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Abstract Helical gears are widely used in gearboxes due to its low noise and high load carrying capacity, but it is difficult to diagnose their early faults based on the signals produced by condition monitoring systems, particularly when the gears rotate at low speed. In this paper, a new concept of Root Mean Square (RMS) value calculation using angle domain signals within small angular ranges is proposed. With this concept, a new diagnosis algorithm based on the time pulses of an encoder is developed to overcome the difficulty of fault diagnosis for helical gears at low rotational speeds. In this proposed algorithm, both acceleration signals and encoder impulse signal are acquired at the same time. The sampling rate and data length in angular domain are determined based on the rotational speed and size of the gear. The vibration signals in angular domain are obtained by re-sampling the vibration signal of the gear in the time domain according to the encoder pulse signal. The fault features of the helical gear at low rotational speed are then obtained with reference to the RMS values in small angular ranges and the order tracking spectrum following the Angular Domain Synchronous Average processing (ADSA). The new algorithm is not only able to reduce the noise and improves the signal to noise ratio by the ADSA method, but also extracts the features of helical gear fault from the meshing position of the faulty gear teeth, hence overcoming the difficulty of fault diagnosis of helical gears rotating at low speed. The experimental results have shown that the new algorithm is more effective than traditional diagnosis methods. The paper concludes that the proposed helical gear fault diagnosis method based on time pulses of encoder algorithm provides a new means of helical gear fault detection and diagnosis.
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paper_title: A heterogeneous fault diagnosis method for bearings in gearbox

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Bearing failure is one of the most important causes of breakdown of rotating machinery. These failures can lead to catastrophic disasters or result in costly downtime. One of the key problems in bearing fault diagnosis is to detect the bearing fault as early as possible. This capability enables the operator to have enough time to do some preventive maintenance. Most papers investigate the bearing faults under rational assumption that bearings work individually. However, bearings are usually working as a part of complex systems like a gearbox. The fault signal of bearings can be easily masked by other vibration generated from gears and shafts. The proposed method separates bearing signals from other signals, and then the optimum frequency band which the bearing fault signal is prominent is determined by mean envelope Kurtosis. Subsequently, the envelope analysis is used to detect the bearing faults. Finally, two bearing fault experiments are used to validate the proposed method. Each experiment contains tw...
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paper_title: Early Classification of Bearing Faults Using Morphological Operators and Fuzzy Inference

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Bearing faults of rotating machinery are observed as impulses in the vibration signal, but it is mostly immersed in noise. In order to effectively remove this noise and detect the impulses, a novel technique with morphological operators and fuzzy inference is proposed in this paper. The effectiveness of the morphological operators lies with the correct selection of structuring elements (SEs). This paper also proposes a new algorithm for this SE selection based on kurtosis, thereby making the analysis free of empirical methods. When analyzed with three different sets of faults, the results show that this method is effective and robust in bringing out the impulses. With fuzzy inference being coupled to this new technique, it makes the algorithm to be able to detect early faults also.
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paper_title: Enhanced DET-Based Fault Signature Analysis for Reliable Diagnosis of Single and Multiple-Combined Bearing Defects

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To early identify cylindrical roller bearing failures, this paper proposes a comprehensive bearing fault diagnosis method, which consists of spectral kurtosis analysis for finding the most informative subband signal well representing abnormal symptoms about the bearing failures, fault signature calculation using this subband signal, enhanced distance evaluation technique- (EDET-) based fault signature analysis that outputs the most discriminative fault features for accurate diagnosis, and identification of various single and multiple-combined cylindrical roller bearing defects using the simplified fuzzy adaptive resonance map (SFAM). The proposed comprehensive bearing fault diagnosis methodology is effective for accurate bearing fault diagnosis, yielding an average classification accuracy of 90.35%. In this paper, the proposed EDET specifically addresses shortcomings in the conventional distance evaluation technique (DET) by accurately estimating the sensitivity of each fault signature for each class. To verify the efficacy of the EDET-based fault signature analysis for accurate diagnosis, a diagnostic performance comparison is carried between the proposed EDET and the conventional DET in terms of average classification accuracy. In fact, the proposed EDET achieves up to 106.85% performance improvement over the conventional DET in average classification accuracy.
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paper_title: Spectral kurtosis based on AR model for fault diagnosis and condition monitoring of rolling bearing

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Spectral kurtosis (SK) is an algorithm that gives an indication of how kurtosis varies with frequency. A frequency band that contains abundant information, especially the impact signal, can be tracked by calculating SK. In the present article, SK combined with Autoregressive AR model, was applied into the fault diagnosis and condition monitoring of bearings. Accelerated life test of rolling bearings in Hangzhou Bearing Test & Research Center (HBRC) was performed to collect vibration data over their entire lifetime (normal-fault-failure). The result shows that SK can detect early incipient fault by eliminating some other interfering frequency components. In addition, it can detect fault 5 min earlier than root mean value (RMS). This fault detection in advance is significant for condition monitoring.
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paper_title: Application of the horizontal slice of cyclic bispectrum in rolling element bearings diagnosis

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Abstract The vibration signals of rolling element bearings are random cyclostationary when they have faults. However, because the background noise is very heavy when the early fault occurs, it is difficult to disclose the latent periodic components successfully even using the second order cyclostationary analysis. To overcome this difficulty, the cyclic bispectrum (CBS), an alternative approach based on bispectrum and cyclostationarity analysis, is discussed in this paper. Furthermore, the slice spectrum analysis of the CBS is proposed. The CBS is a third-order cyclic statistical parameter, in the frequency domain. The CBS gives full play to the advantage, which is provided from the higher order cyclic statistical methods. It can restrain noise and provide more information than classical methods such as amplitude spectrum analysis and envelope analysis when the fault is at an early stage. However, the CBS is four-dimensional. So, the Slices Spectrum Analysis of the CBS is introduced to fault diagnosis. Firstly, the slice of the CBS, which is 3D structure, is sliced along the cyclic frequency axis. The CBS corresponding to one cyclic frequency is called the once slice of the CBS. However the information in the once slice is redundant and indirect for fault diagnosis. So the twice slice of the CBS, which is sliced along one frequency axis is studied. By analysis, the horizontal slice of the once slice of the CBS (HSCBS), which is at a special given cyclic frequency is proposed to resolve the contradiction. Simulation and experiment of the rolling element bearing fault diagnosis are performed, and the results indicate the feasibility and validity of the HSCBS analysis in rolling element bearing early fault diagnosis.
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paper_title: Single point bearing fault diagnosis using simplified frequency model

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The time synchronous averaging (TSA) is a well-known technique used for early detection of bearing failure in electrical machines. This method is very efficient if the characteristic default frequency is perfectly known. In this article, a reduced frequency model, derived from ESPRIT algorithm, is used to provide a very accurate estimation of the fault frequency. The precision obtained on this frequency allows to apply TSA algorithm under optimal conditions. The proposed method is tested on simulated and real vibration signals for inner and outer ring faults. Finally, a fault indicator is proposed to discriminate the healthy case from the faulty one.
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paper_title: Study on Fault Diagnosis of Rolling Bearing Based on Time-Frequency Generalized Dimension

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The condition monitoring technology and fault diagnosis technology of mechanical equipment played an important role in the modern engineering. Rolling bearing is the most common component of mechanical equipment which sustains and transfers the load. Therefore, fault diagnosis of rolling bearings has great significance. Fractal theory provides an effective method to describe the complexity and irregularity of the vibration signals of rolling bearings. In this paper a novel multifractal fault diagnosis approach based on time-frequency domain signals was proposed. The method and numerical algorithm of Multi-fractal analysis in time-frequency domain were provided. According to grid type J and order parameter q in algorithm, the value range of J and the cut-off condition of q were optimized based on the effect on the dimension calculation. Simulation experiments demonstrated that the effective signal identification could be complete by multifractal method in time-frequency domain, which is related to the factors such as signal energy and distribution. And the further fault diagnosis experiments of bearings showed that the multifractal method in time-frequency domain can complete the fault diagnosis, such as the fault judgment and fault types. And the fault detection can be done in the early stage of fault. Therefore, the multifractal method in time-frequency domain used in fault diagnosis of bearing is a practicable method.
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paper_title: Anomaly detection of cooling fan and fault classification of induction motor using Mahalanobis-Taguchi system

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A health index, Mahalanobis distance (MD), is proposed to indicate the health condition of cooling fan and induction motor based on vibration signal. Anomaly detection and fault classification are accomplished by comparing MDs, which are calculated based on the feature data set extracted from the vibration signals under normal and abnormal conditions. Since MD is a non-negative and non-Gaussian distributed variable, Box-Cox transformation is used to convert the MDs into normal distributed variables, such that the properties of normal distribution can be employed to determine the ranges of MDs corresponding to different health conditions. Experimental data of cooling fan and induction motor are used to validate the proposed approach. The results show that the early stage failure of cooling fan caused by bearing generalized-roughness faults can be detected successfully, and the different unbalanced electrical faults of induction motor can be classified with a higher accuracy by Mahalanobis-Taguchi system. Such works could aid in the reliable operation of the machines, the reduction of the unexpected failures, and the improvement of the maintenance plan.
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paper_title: Gear crack detection using modified TSA and proposed fault indicators for fluctuating speed conditions

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Abstract Fault diagnosis of gearbox at an early stage is very important to avoid catastrophic failures. Condition indicators (CI) are used to quantify the level of vibration generated by the defected gears. A comprehensive comparison of various CIs, i.e., RMS, kurtosis, crest factor, FM0, FM4, M6A, NB4, energy ratio, NA4, energy operator and two new proposed condition indicators (PS-I and PS-II), have been performed for no crack, initial crack and advanced crack for different fluctuating profiles of input speed. This comparative analysis shows the responsiveness of indicators towards crack detection. Limited speed fluctuations and instantaneous high speed fluctuations are considered. Results suggest that newly proposed condition indicators are more robust, stable and effective towards crack detection under fluctuating profiles of speed. A modified time synchronous averaging (MTSA) is also proposed to increase signal-to-noise ratio (SNR).
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paper_title: Novel method for rolling element bearing health assessment—A tachometer-less synchronously averaged envelope feature extraction technique

paper_content:

Abstract The assessment and diagnosis of bearing health using vibration data has been a research topic of interest for many years and includes developments in an assortment of signal processing methods and classification algorithms. This paper investigates detecting bearing degradation at different levels of damage, in that estimating the bearing health at the various stages of degradation is important for predicting failure as well as making maintenance decisions. The proposed technique does not require a measure of the rotational shaft speed or bearing cage speed, which makes it very suitable in certain applications in which it is very difficult or not cost effective to measure the rotational speed. To effectively estimate the bearing health state, a novel tachometer-less synchronously averaged envelope (TLSAE) signal processing and feature extraction technique for rolling element bearing is proposed. The Tachometer-Less Synchronous Averaged Envelope (TLSAE) method consists of first using a narrow band pass filter around a calculated bearing fault frequency of interest and using the derivative of the phase of the Hilbert Transform of this narrow band signal to generate a synthesized tachometer signal that is representative of the impact due to a bearing defect. This synthesized tachometer signal is combined with the high frequency envelope method to perform synchronous averaging on the envelope signal, resulting in a defect synchronous envelope spectrum in which the frequency content is in terms of the fault frequency orders. The proposed method is further compared and evaluated with other existing methods, in particular to the traditional Fourier Transform technique, the bearing envelope analysis technique, and the empirical mode decomposition signal processing methods on the basis of whether each method provides an enhanced level of indication that can determine the health of rolling element bearings. Data from a bearing test-rig is used to facilitate the comparison and evaluation of the signal processing methods. Vibration data was collected from the test-rig for bearings with different levels of degradation. The calculated vibration features from the tachometer-less synchronously averaged envelope (TLSAE) technique are compared to the other feature extraction techniques; with the synchronous average method providing a set of bearing vibration features that can distinguish all three levels of damage on the outer race of the rolling element bearing. Future work looks to further investigate this proposed technique for data collected during a run to failure test in order to consider its merits for early detection of incipient bearing damage and whether it provides a consistent monotonic trend from spall initiation to bearing failure.
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paper_title: Analysing RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes

paper_content:

Wind turbines (WTs) are designed to operate under extreme environmental conditions. This means that extreme and varying loads experienced by WT components need to be accounted for as well as gaining access to wind farms (WFs) at different times of the year. Condition monitoring (CM) is used by WF owners to assess WT health by detecting gearbox failures and planning for operations and maintenance (O&M). However, there are several challenges and limitations with commercially available CM technologies – ranging from the cost of installing monitoring systems to the ability to detect faults accurately. This study seeks to address some of these challenges by developing novel techniques for fault detection using the RMS and Extreme (peak) values of vibration signals. The proposed techniques are based on three models (signal correlation, extreme vibration, and RMS intensity) and have been validated with a time domain data driven approach using CM data of operational WTs. The findings of this study show that monitoring RMS and Extreme values serves as a leading indicator for early detection of faults using Extreme value theory, giving WF owners time to schedule O&M. Furthermore, it also indicates that the prediction accuracy of each CM technique depends on the physics of failure. This suggests that an approach which incorporates the strengths of multiple techniques is needed for holistic health assessment of WT components.
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paper_title: Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive Fuzzy -Means Clustering

paper_content:

Vibration signal analysis is one of the most effective methods for mechanical fault diagnosis. Available part of the information is always concealed in component noise, which makes it much more difficult to detect the defection, especially at early stage of the development. This paper presents a new approach for mechanical fault diagnosis based on time domain analysis and adaptive fuzzy -means clustering. By analyzing vibration signal collected, nine common time domain parameters are calculated. This lot of data constitutes data matrix as characteristic vectors to be detected. And using adaptive fuzzy -means clustering, the optimal clustering number can be gotten then to recognize different fault types. Moreover, five parameters, including variance, RMS, kurtosis, skewness, and crest factor, of the nine are selected as the new eigenvector matrix to be clustered for more optimal clustering performance. The test results demonstrate that the proposed approach has a sensitive reflection towards fault identifications, including slight fault.
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paper_title: Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution

paper_content:

The early fault characteristics of rolling element bearings carried by vibration signals are quite weak because the signals are generally masked by heavy background noise. To extract the weak fault characteristics of bearings from the signals, an improved spectral kurtosis (SK) method is proposed based on maximum correlated kurtosis deconvolution (MCKD). The proposed method combines the ability of MCKD in indicating the periodic fault transients and the ability of SK in locating these transients in the frequency domain. A simulation signal overwhelmed by heavy noise is used to demonstrate the effectiveness of the proposed method. The results show that MCKD is beneficial to clarify the periodic impulse components of the bearing signals, and the method is able to detect the resonant frequency band of the signal and extract its fault characteristic frequency. Through analyzing actual vibration signals collected from wind turbines and hot strip rolling mills, we confirm that by using the proposed method, it is possible to extract fault characteristics and diagnose early faults of rolling element bearings. Based on the comparisons with the SK method, it is verified that the proposed method is more suitable to diagnose early faults of rolling element bearings.
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paper_title: Early fault detection in gearboxes based on support vector machines and multilayer perceptron with a continuous wavelet transform

paper_content:

The study was undertaken to evaluate early fault detection of a gearbox using ANN.Selected coefficients from CWT were used as input data for the neural networks. One of the research problems investigated these days is early fault detection. To this end, advanced signal processing algorithms are employed. The present paper makes an attempt at early fault detection in a gearbox. In order to evaluate its technical condition, artificial neural networks were used. Early fault detection based on support vector machines is a relatively new and rarely employed method for evaluating condition of machines, particularly gearboxes. The available literature offers very promising results of using this method. In order to compare the obtained results, a multilayer perceptron network was created. Such standard neural network ensures high effectiveness. The vibration signal obtained from a sensor is seldom a material for direct analysis. First, it needs to be processed to bring out the informative part of the signal. To this end, a wavelet transform was used. The presented results concern both a "raw" vibration signal and processed one, investigated for two neural networks. The wavelet transform has proved to improve significantly the accuracy of condition evaluation and the results obtained by the two networks are consistent with one another.
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paper_title: Multi-layer neural network with deep belief network for gearbox fault diagnosis

paper_content:

Identifying gearbox damage categories, especially for early faults and combined faults, is a challenging task in gearbox fault diagnosis. This paper presents multiple classifiers based on multi-layer neural networks (MLNN) to implement vibration signals for fault diagnosis in gearbox. A MLNN-based learning architecture using deep belief network (MLNNDBN) is proposed for gearbox fault diagnosis. Training process of the proposed learning architecture includes two stages: A deep belief network is constructed firstly, and then is trained; after a certain amount of epochs, the weights of deep belief network are used to initialize the weights of the constructed MLNN; at last, the trained MLNN is used as classifiers to classify gearbox faults. Multidimensional feature sets including time-domain, frequency-domain features are extracted to reveal gear health conditions. Experiments with different combined faults were conducted, and the vibration signals were captured under different loads and motor speeds. To confirm the superiority of MLNNDBN in fault classification, its performance is compared with other MLNN-based methods with different fine-tuning schemes and relevant vector machine. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery.
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paper_title: Early fault detection of rotating machinery through chaotic vibration feature extraction of experimental data sets

paper_content:

Abstract Fault detection of rotating machinery by the complex and non-stationary vibration signals with noise is very difficult, especially at the early stages. Also, many failure mechanisms and various adverse operating conditions in rotating machinery involve significant nonlinear dynamical properties. As a novel method, phase space reconstruction is used to study the effect of faults on the chaotic behavior, for the first time. Strange attractors in reconstructed phase space proof the existence of chaotic behavior. To quantify the chaotic vibration for fault diagnosis, a set of new features are extracted. These features include the largest Lyapunov exponent; approximate entropy and correlation dimension which acquire more fault characteristic information. The variations of these features for different healthy/faulty conditions are very good for fault diagnosis and identification. For the first time, a new chaotic feature space is introduced for fault detection, which is made from chaotic behavior features. In this space, different conditions of rotating machinery are separated very well. To obtain more generalized results, the features are introduced into a neural network to identify different faults in rotating machinery. The effectiveness of the new features based on chaotic vibrations is demonstrated by the experimental data sets. The proposed approach can reliably recognize different fault types and have more accurate results. Also, the performance of the new procedure is robust to the variation of load values and shows good generalization capability for various load values.
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paper_title: Bearing Fault Detection by One-Dimensional Convolutional Neural Networks

paper_content:

Bearing faults are the biggest single source of motor failures. Artificial Neural Networks (ANNs) and other decision support systems are widely used for early detection of bearing faults. The typical decision support systems require feature extraction and classification as two distinct phases. Extracting fixed features each time may require a significant computational cost preventing their use in real-time applications. Furthermore, the selected features for the classification phase may not represent the most optimal choice. In this paper, the use of 1D Convolutional Neural Networks (CNNs) is proposed for a fast and accurate bearing fault detection system. The feature extraction and classification phases of the bearing fault detection are combined into a single learning body with the implementation of 1D CNN. The raw vibration data (signal) is fed into the proposed system as input eliminating the need for running a separate feature extraction algorithm each time vibration data is analyzed for classification. Implementation of 1D CNNs results in more efficient systems in terms of computational complexity. The classification performance of the proposed system with real bearing data demonstrates that the reduced computational complexity is achieved without a compromise in fault detection accuracy.
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paper_title: Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network

paper_content:

Abstract After analyzing the shortcomings of current feature extraction and fault diagnosis technologies, a new approach based on wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) are combined to extract fault feature frequency and neural network for rotating machinery early fault diagnosis is proposed. Acquisition signals with fault frequency feature are decomposed into a series of narrow bandwidth using WPD method for de-noising, then, the intrinsic mode functions (IMFs), which usually denoted the features of corresponding frequency bandwidth can be obtained by applying EMD method. Thus, the component of IMF with signal feature can be separated from all IMFs and the energy moment of IMFs is proposed as eigenvector to effectively express the failure feature. The classical three layers BP neural network model taking the fault feature frequency as target input of neural network, the 5 spectral bandwidth energy of vibration signal spectrum as characteristic parameter, and the 10 types of representative rotor fault as output can be established to identify the fault pattern of a machine. Lastly, the fault identification model of rotating machinery with rotor lateral early crack based on BP neural network is taken as an example. The results show that the proposed method can effectively get the signal feature to diagnose the occurrence of early fault of rotating machinery.
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paper_title: Applying the fuzzy lattice neurocomputing (FLN) classifier model to gear fault diagnosis

paper_content:

Gear faults are among the main causes of rotating machines breakdown in industrial applications. Intelligent condition monitoring for fault diagnosis can be helpful for detecting gear faults in an early stage so as to reduce production loss and, in addition, improve operation safety and reliability. In this work, we present an intelligent gear fault diagnosis scheme based on a novel classification model, namely the fuzzy lattice neurocomputing (FLN) classifier model. Five gear states including one healthy state and four defective states are tested in a two-stage gearbox. Statistical parameters in both the time domain and the frequency domain of vibration signals, acquired from gearbox, are used as features. We conducted experiments on a benchmark dataset as well as on a gear faults dataset to evaluate both the classification performance and the computational cost of the FLN classifier comparatively with alternative classification methods from the literature including artificial neural networks, support vector machines and decision trees. Our study has demonstrated that the FLN model yields better classification performance with smaller computational cost than the aforementioned alternative methods. The FLN classifier can further be used for condition monitoring and fault diagnosis in other mechanical systems.
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paper_title: Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron

paper_content:

Rolling element bearings are critical mechanical components in rotating machinery and fault detection in the early stages of damage is important to prevent their malfunctioning and failure. Vibration monitoring is the most widely used and cost-effective monitoring technique to detect, locate and distinguish faults in rolling element bearings. This paper purposes single hidden layer architecture for fault diagnosis of rolling element bearings. The particular of this proposed architecture is its ability to generalize for solving both basic classification and fault identification. The network uses the features of time-domain vibration signals with normal and defective bearings. The Multi Layer Perceptron (MLP) was trained and tested with a set of experimental data obtained from previous experiments developed by FEG, CWRU and RANDALL laboratories. The results show the effectiveness of the MLP to diagnose the machine condition for the various data used.
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paper_title: Intelligent Diagnosis Method for Rotating Machinery Using Wavelet Transform and Ant Colony Optimization

paper_content:

This paper proposes an intelligent diagnosis method for condition diagnosis of rotating machinery by using wavelet transform (WT) and ant colony optimization (ACO), in order to detect faults and distinguish fault types at an early stage. The WT is used to extract a feature signal of each machine state from a measured vibration signal for for high-accuracy condition diagnosis. The decision method of optimum frequency area for the extraction of the feature signal is discussed by using real plant data. We convert the state identification for the condition diagnosis of rotating machinery to a clustering problem of the values of the nondimensional symptom parameters (NSPs). ACO is introduced for this purpose. NSPs are calculated with the recomposed signals of each frequency level. These parameters can reflect the characteristics of the signals measured for the condition diagnosis. The synthetic detection index (SDI), on the basis of statistical theory, is defined to evaluate the applicability of the NSPs. The SDI can be used to select better NSPs for the ACO. Practical examples of diagnosis for a bearing used in the centrifugal fan system are shown to verify the effectiveness of the methods proposed in this paper.
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paper_title: Online feature learning for condition monitoring of rotating machinery

paper_content:

Abstract Condition-based maintenance of rotating machinery requires efficient condition monitoring methods that enable early detection of abnormal operational conditions and faults. This is a challenging problem because machines are different and change characteristics over time due to wear and maintenance. The efficiency and scalability of conventional condition monitoring methods are limited by the need for manual analysis and re-configuration. The problem to extract relevant features from condition monitoring signals and thereby detect and analyze changes in such signals is a central issue, which in principle can be addressed using machine learning methods. Former work demonstrates that dictionary learning can be used to automatically derive signal features that characterize different operational conditions and faults of a rotating machine, but the use of such methods for online condition monitoring purposes is an open problem. Here we investigate online learning of features using dictionary learning. We describe dictionary distance and signal fidelity based heuristics for anomaly detection, and we study the time-propagated features and sparse approximation of vibration and acoustic emission signals in three different case studies. We present results of numerical experiments with different hyperparameters affecting the approximation accuracy, computational cost, and the adaptation rate of the learned features. We find that the learned features change slowly under normal variations of the operational conditions in comparison to the rapid adaptation observed when a fault appears (bearing defects, magnetite particles in the lubricant, or plastic deformation of steel). Furthermore, we find that a sparse signal approximation with 2.5% preserved coefficients based on a propagated dictionary is sufficient for bearing defect detection.
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paper_title: Fault detection via recurrence time statistics and one-class classification

paper_content:

A methodology for fault detection in rotating machinery is presented.An original one-class classifier based on extreme statistics (EVOC) is employed.One advantage of the method is a reduced number of hyperparameters to be adjusted.Another advantage is the use of only normal data of the machinery being monitored.The method shows higher classification accuracy than other state-of-the-art methods. Predictive maintenance has emerged as a fundamental practice to preserve production assets in many industrial environments. Of a wide set of approaches, vibration analysis is one of the most used for high-speed rotating machinery, especially when fault detection is to be automatic. Traditionally, this task has been studied as a classification problem using data extracted from the frequency domain. This approach, however, has two main limitations: (a) manufacture and mounting procedures can vary the vibration spectra of a machine, even when these share the same design; and (b) incipient fault signatures may be concealed in the frequency domain by noise and vibration from other parts of the system. For these reasons, the application of a classifier obtained for one machine to another machine is pointless, making early fault detection difficult. In this paper, a bearing fault detection problem is tackled using one-class classifiers and features extracted from vibration capture in the time domain using recurrence time statistics. We also describe a study of the behavior of the proposed method in real conditions. Our method shows high detection accuracy accompanied by a reduced number of false positives and negatives.
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paper_title: Automatic progressive damage detection of rotor bar in induction motor using vibration analysis and multiple classifiers

paper_content:

There is an increased interest in developing reliable condition monitoring and fault diagnosis systems of machines like induction motors; such interest is not only in the final phase of the failure but also at early stages. In this paper, several levels of damage of rotor bars under different load conditions are identified by means of vibration signals. The importance of this work relies on a simple but effective automatic detection algorithm of the damage before a break occurs. The feature extraction is based on discrete wavelet analysis and autocorrelation process. Then, the automatic classification of the fault degree is carried out by a binary classification tree. In each node, comparing the learned levels of the breaking off correctly identifies the fault degree. The best results of classification are obtained employing computational intelligence techniques like support vector machines, multilayer perceptron, and the k-NN algorithm, with a proper selection of their optimal parameters.
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paper_title: Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery

paper_content:

Abstract Early fault detection and diagnosis plays an increasingly important role in various energy systems where it is critical to avoid deteriorating condition, degraded efficiency and unexpected failures. Rolling-element bearings are among the most common components of rotating machinery used for transformation of energy. Mechanical wear and defective bearings cause rotating machinery to decrease its efficiency, and thus increase energy consumption. A new technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis is presented. After normalization and the wavelet transform of vibration signals, the standard deviation as a measure of average energy and the logarithmic energy entropy as a measure of the degree of disorder are extracted in sub-bands of interest as representative features. Then the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection and diagnosis is performed by quadratic classifiers. Accuracy of the new technique was tested on four classes of the recorded vibrations signals, i.e. normal, with the fault of inner race, outer race and balls operation. An overall accuracy of 100% was achieved. The new technique will be further tested and implemented in a real production environment.
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Title: A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Section 1: Introduction
Description 1: Introduce the significance of early fault diagnosis in rotating machinery and its importance in preventing catastrophic failures.

Section 2: Empirical Mode Composition
Description 2: Describe the Empirical Mode Composition (EMD) method and its applications in fault diagnosis of rotating machinery.

Section 3: Ensemble Empirical Mode Composition
Description 3: Explain the Ensemble Empirical Mode Composition (EEMD) method, its improvements over EMD, and its use in early fault diagnosis.

Section 4: Local Mean Decomposition
Description 4: Outline the Local Mean Decomposition (LMD) technique and its effectiveness in decomposing signals for fault diagnosis.

Section 5: Empirical Wavelet Transform
Description 5: Discuss the Empirical Wavelet Transform (EWT) and its ability to extract AM-FM components from signals for fault detection.

Section 6: Variational Mode Decomposition
Description 6: Explain the Variational Mode Decomposition (VMD) method and its applications in detecting and diagnosing faults in rotating machinery.

Section 7: Wavelet Transform
Description 7: Review the use of various wavelet transform methods (e.g., CWT, DWT, WPT) in analyzing transient signals for fault diagnosis.

Section 8: Sparse Decomposition
Description 8: Describe the principles of sparse representation and its applications in early fault diagnosis of rotating machinery.

Section 9: AI-Based Early Fault Diagnosis
Description 9: Introduce AI-based early fault diagnosis methods, including data acquisition, fault feature extraction, and fault pattern identification.

Section 10: KNN
Description 10: Describe the K-nearest neighbors (KNN) algorithm, its application in fault classification, and typical examples of its use.

Section 11: SVM
Description 11: Explain the Support Vector Machine (SVM) method, its approach to fault classification, and examples of its application.

Section 12: Neural Network
Description 12: Discuss the use of neural networks in fault diagnosis, including BP neural networks, their structure, and application examples.

Section 13: Applications of FFD in Early Fault Diagnosis of Rotating Machinery
Description 13: Review various frequency domain-based methods (FFD) employed in fault diagnosis and their specific applications.

Section 14: Discussion and Conclusions
Description 14: Summarize the major findings from the review, discuss the limitations of current methods, and suggest future research directions.

Section 15: Summary
Description 15: Provide a concise overview of the review's content, highlighting the importance of early fault diagnosis in rotating machinery and summarizing the key methods discussed.