A survey on diagnostics methods for automotive engines
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paper_title: A review of condition monitoring and fault diagnosis for diesel engines

paper_content:

Technical advances and environmental legislation in recent years have stimulated the development of a number of techniques for condition monitoring and fault diagnosis (CMFD) in diesel engines. This paper firstly summarises common faults, fault mechanisms and their effect on diesel engine performance. Corresponding measurands are presented. Standard CMFD methods for parameters and CMFD systems for diesel engines are reviewed. Finally, some advanced CMFD techniques, including neural networks and fuzzy logic, which may be more powerful, are discussed.
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paper_title: Diagnosis and Prognosis of Automotive Systems: motivations, history and some results

paper_content:

Abstract The field of automotive engineering has seen an explosion in the presence of on-board electronic components and systems vehicles since the 1970s. This growth was initially motivated by the introduction of emissions regulations that led to the widespread application of electronic engine controls. A secondary but important consequence of these developments was the adoption of on-board diagnostics regulations aimed at insuring that emission control systems would operate as intended for a prescribed period of time (or vehicle mileage). In addition, the presence of micro-controllers on-board the vehicle has led to a proliferation of other functions related to safety and customer convenience, and implemented through electronic systems and related software, thus creating the need for more sophisticated on-board diagnostics. Today, a significant percentage of the software code in an automobile is devoted to diagnostic functions. This paper presents an overview of diagnostic needs and requirements in the automotive industry, illustrates some of the challenges that are associated with satisfying these requirements, and proposes some future directions.
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paper_title: Robust Model-Based Fault Diagnosis for Dynamic Systems

paper_content:

There is an increasing demand for dynamic systems to become safer and more reliable. This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital. It is clear that fault diagnosis is becoming an important subject in modern control theory and practice. Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework. It contains many important topics and methods; however, total coverage and completeness is not the primary concern. The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches. In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications. The first two chapters are of tutorial value and provide a starting point for newcomers to this field. The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications. This will certainly appeal to experts in this field. Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research. The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed. Although this is a research monograph, it will be an important text for postgraduate research students world-wide. The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world.
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paper_title: Fault Detection and Diagnosis in Industrial Systems

paper_content:

The appearance of this book is quite timely as it provides a much needed state-of-the-art exposition on fault detection and diagnosis, a topic of much interest to industrialists. The material included is well organized with logical and clearly identified parts; the list of references is quite comprehensive and will be of interest to readers who wish to explore a particular subject in depth. The presentation of the subject material is clear and concise, and the contents are appropriate to postgraduate engineering students, researchers and industrialists alike. The end-of-chapter homework problems are a welcome feature as they provide opportunities for learners to reinforce what they learn by applying theory to problems, many of which are taken from realistic situations. However, it is felt that the book would be more useful, especially to practitioners of fault detection and diagnosis, if a short chapter on background statistical techniques were provided. Joe Au
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paper_title: Data-driven methods for fault detection and diagnosis in chemical processes

paper_content:

I. Introduction.- 1. Introduction.- II. Background.- 2. Multivariate Statistics.- 3. Pattern Classification.- III. Methods.- 4. Principal Component Analysis.- 5. Fisher Discriminant Analysis.- 6. Partial Least Squares.- 7. Canonical Variate Analysis.- IV. Application.- 8. Tennessee Eastman Process.- 9. Application Description.- 10. Results and Discussion.- V. Other Approaches.- 11. Overview of Analytical and Knowledge-based Approaches.- References.
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paper_title: Statistical process control of multivariate processes

paper_content:

Abstract With process computers routinely collecting measurements on large numbers of process variables, multivariate statistical methods for the analysis, monitoring and diagnosis of process operating performance have received increasing attention. Extensions of traditional univariate Shewhart, CUSUM and EWMA control charts to multivariate quality control situations are based on Hotelling's T 2 statistic. Recent approaches to multivariate statistical process control which utilize not only product quality data (Y), but also all of the available process variable data (X) are based on multivariate statistical projection methods (Principal Component Analysis (PCA) and Partial Least Squares (PLS)). This paper gives an overview of these methods, and their use for the statistical process control of both continuous and batch multivariate processes. Examples are provided of their use for analysing the operations of a mineral processing plant, for on-line monitoring and fault diagnosis of a continuous polymerization process and for the on-line monitoring of an industrial batch polymerization reactor.
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paper_title: Nonlinear PCA With the Local Approach for Diesel Engine Fault Detection and Diagnosis

paper_content:

This brief examines the application of nonlinear statistical process control to the detection and diagnosis of faults in automotive engines. In this statistical framework, the computed score variables may have a complicated nonparametric distribution function, which hampers statistical inference, notably for fault detection and diagnosis. This brief shows that introducing the statistical local approach into nonlinear statistical process control produces statistics that follow a normal distribution, thereby enabling a simple statistical inference for fault detection. Further, for fault diagnosis, this brief introduces a compensation scheme that approximates the fault condition signature. Experimental results from a Volkswagen 1.9-L turbo-charged diesel engine are included.
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paper_title: Fault diagnosis in internal combustion engines using non-linear multivariate statistics

paper_content:

AbstractThis paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce non-linear relationships between the recorded engine variables, the paper proposes the use of non-linear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new non-linear variable reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady state operating conditions. More precisely, non-linear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause o...
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paper_title: Principal Component Analysis

paper_content:

When large multivariate datasets are analyzed, it is often desirable to reduce their dimensionality. Principal component analysis is one technique for doing this. It replaces the p original variables by a smaller number, q, of derived variables, the principal components, which are linear combinations of the original variables. Often, it is possible to retain most of the variability in the original variables with q very much smaller than p. Despite its apparent simplicity, principal component analysis has a number of subtleties, and it has many uses and extensions. A number of choices associated with the technique are briefly discussed, namely, covariance or correlation, how many components, and different normalization constraints, as well as confusion with factor analysis. Various uses and extensions are outlined. ::: ::: ::: Keywords: ::: ::: dimension reduction; ::: factor analysis; ::: multivariate analysis; ::: variance maximization
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paper_title: Application of a data-driven monitoring technique to diagnose air leaks in an automotive diesel engine: A case study

paper_content:

This paper presents a case study of the application of a data-driven monitoring technique to diagnose air leaks in an automotive diesel engine. Using measurement signals taken from the sensors/actuators which are present in a modern automotive vehicle, a data-driven diagnostic model is built for condition monitoring purposes. Detailed investigations have shown that measured signals taken from the experimental test-bed often contain redundant information and noise due to the nature of the process. In order to deliver a clear interpretation of these measured signals, they therefore need to undergo a 'compression' and an 'extraction' stage in the modelling process. It is at this stage that the proposed data-driven monitoring technique plays a significant role by taking only the important information of the original measured signals for fault diagnosis purposes. The status of the engine's performance is then monitored using this diagnostic model. This condition monitoring process involves two separate stages of fault detection and root-cause diagnosis. ::: ::: The effectiveness of this diagnostic model was validated using an experimental automotive 1.9L four-cylinder diesel engine embedded in a chassis dynamometer in an engine test-bed. Two joint diagnostics plots were used to provide an accurate and sensitive fault detection process. Using the proposed model, small air leaks in the inlet manifold plenum chamber with a diameter size of 2-6 turn were accurately detected. Further analyses using contribution to T-2 and Q statistics show the effect of these air leaks on fuel consumption. It was later discovered that these air leaks may contribute to emissions fault. ::: ::: In comparison to the existing model-based approaches, the proposed method has several benefits: (i) it makes no simplifying assumptions, as the model is built entirely from the measured signals; (ii) it is simple and straight-forward; (iii) there is no additional hardware required for modelling; (iv) it is a time and cost-efficient way to deliver condition monitoring (i.e. fault diagnosis application); (v) it is capable of pin-pointing the root-cause and the effect of the problem; and (vi) it is feasible to be implemented in practice. (c) 2005 Elsevier Ltd. All rights reserved.
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paper_title: Diagnosis and Prognosis of Automotive Systems: motivations, history and some results

paper_content:

Abstract The field of automotive engineering has seen an explosion in the presence of on-board electronic components and systems vehicles since the 1970s. This growth was initially motivated by the introduction of emissions regulations that led to the widespread application of electronic engine controls. A secondary but important consequence of these developments was the adoption of on-board diagnostics regulations aimed at insuring that emission control systems would operate as intended for a prescribed period of time (or vehicle mileage). In addition, the presence of micro-controllers on-board the vehicle has led to a proliferation of other functions related to safety and customer convenience, and implemented through electronic systems and related software, thus creating the need for more sophisticated on-board diagnostics. Today, a significant percentage of the software code in an automobile is devoted to diagnostic functions. This paper presents an overview of diagnostic needs and requirements in the automotive industry, illustrates some of the challenges that are associated with satisfying these requirements, and proposes some future directions.
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paper_title: Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis

paper_content:

Abstract Principal component analysis (PCA) is the most commonly used dimensionality reduction technique for detecting and diagnosing faults in chemical processes. Although PCA contains certain optimality properties in terms of fault detection, and has been widely applied for fault diagnosis, it is not best suited for fault diagnosis. Discriminant partial least squares (DPLS) has been shown to improve fault diagnosis for small-scale classification problems as compared with PCA. Fisher's discriminant analysis (FDA) has advantages from a theoretical point of view. In this paper, we develop an information criterion that automatically determines the order of the dimensionality reduction for FDA and DPLS, and show that FDA and DPLS are more proficient than PCA for diagnosing faults, both theoretically and by applying these techniques to simulated data collected from the Tennessee Eastman chemical plant simulator.
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paper_title: Overview and Recent Advances in Partial Least Squares

paper_content:

Partial Least Squares (PLS) is a wide class of methods for modeling relations between sets of observed variables by means of latent variables. It comprises of regression and classification tasks as well as dimension reduction techniques and modeling tools. The underlying assumption of all PLS methods is that the observed data is generated by a system or process which is driven by a small number of latent (not directly observed or measured) variables. Projections of the observed data to its latent structure by means of PLS was developed by Herman Wold and coworkers [48,49,52].
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paper_title: Application of Partial Least-squares Regression to oil atomic emitting spectrum data of a type diesel engine

paper_content:

Aiming at relation between the concentrations of wearing elements of diesel engine and it's loads(X 1 ), cylinders' clearances(X 2 , X 3 and X 4 ) and runtime after renewing oil(X 5 ), the Partial Least-squares Regression(PLSR) has been used to analyze the oil atomic emitting spectrum data of a 6-cylinder diesel engine. The results show that Cu concentrations variance explained by the five components is largest. These components are derived from X 1 , X 2 , X 3 , X 4 and X 5 . The PLSR-function concerning Cu can forecast Cu concentrations well. It has proved perfect in forecasting all the Cu concentrations of the 69 samples in the seven kinds of operating conditions. The effect of X 1 , X 2 , X 3 , X 4 and X 5 upon Cu concentrations has been effectively evaluated by the Variable Important in Projection (VIP). As compared with the obvious effect of cylinder's clearances(X 2 , X 3 and X 4 ) and that of runtime(X 5 ), the effect of the loads is small(X 1 ).
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paper_title: Data-driven methods for fault detection and diagnosis in chemical processes

paper_content:

I. Introduction.- 1. Introduction.- II. Background.- 2. Multivariate Statistics.- 3. Pattern Classification.- III. Methods.- 4. Principal Component Analysis.- 5. Fisher Discriminant Analysis.- 6. Partial Least Squares.- 7. Canonical Variate Analysis.- IV. Application.- 8. Tennessee Eastman Process.- 9. Application Description.- 10. Results and Discussion.- V. Other Approaches.- 11. Overview of Analytical and Knowledge-based Approaches.- References.
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paper_title: Identification and monitoring of automotive engines

paper_content:

The objective of this paper is to extend and refine the nonlinear canonical variate analysis (NLCVA) methods developed in the previous work for system identification and monitoring of automotive engines. The use of additional refinements in the nonlinear modeling are developed including the use of more general bases of nonlinear functions. One such refinement in the NLCVA system identification is the selection of basis functions using the method of Leaps and Bounds with the Akaike information criterion AIC. Delay estimation procedures are used to considerably reduce the state order of the identified engine models. This also considerably reduces the number of estimated parameters that directly affects the identified model accuracy. This increased accuracy also affects the ability to monitor changes or faults in dynamic engine characteristics. A further objective of this paper is the development and use of nonlinear monitoring methods as extensions of several previously used linear CVA monitoring procedures. For the case of linear Gaussian systems, these monitoring methods have optimal properties in detecting faults or system changes in terms of the general maximum likelihood method. In the nonlinear case, departures from optimality are investigated, but the procedure is shown to still work quite effectively for detecting and identifying system faults and changes.
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paper_title: Fault Detection Using Canonical Variate Analysis

paper_content:

The system identification method canonical variate analysis (CVA) has attracted much attention from researchers for its ability to identify multivariable state-space models using experimental data. A model identified using CVA can use several methods for fault detection. Two standard methods are investigated in this paper: the first is based on Kalman filter residuals for the CVA model, the second on canonical variable residuals. In addition, a third method is proposed that uses the local approach for detecting changes in the canonical variable coefficients. The detection methods are evaluated using three simulation examples; the examples consider the effects of feedback control; process nonlinearities; and multivariable, serially correlated data. The simulations consider several types of common process faults, including sensor faults, load disturbances, and process changes. The simulation results indicate that the local approach provides a very sensitive method for detecting process changes that are dif...
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paper_title: NEURAL NETWORK APPLICATIONS FOR MODEL BASED FAULT DETECTION WITH PARITY EQUATIONS

paper_content:

Abstract The rising complexity of modern automotive engines with an increasing number of actuators and sensors to minimise emissions and fuel consumption and to maximise engine driveability require a detailed supervision for fault detection and on-board diagnosis. The European Community Directive 98/69/EC requires on-board diagnosis for spark ignition engines and will require it for diesel engines as of January 2003, mainly to prevent excessive emissions. Beside this regulation it is also in the interest of the automobile manufactures to establish capable diagnosis systems for maintenance, repair and the benefit of their customers. This paper will describe applications of neural networks for modelling complex fluid- and thermodynamics with unknown physical model structure. Reference models, which describe the fault free process, are set up and identified with the special neural network LOLIMOT (Local-Linear-Model-Tree). Fault detection algorithms, which employ the method of parity equations, were successfully implemented and tested in real time with a 2 litre diesel engine and a Rapid Control Prototyping System. Measurements of online fault detection are shown for several built-in faults in the intake system of this diesel engine.
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paper_title: Model-based diagnosis of large diesel engines based on angular speed variations of the crankshaft

paper_content:

This work aims at monitoring large diesel engines by analyzing the crankshaft angular speed variations. It focuses on a powerful 20-cylinder diesel engine with crankshaft natural frequencies within the operating speed range. First, the angular speed variations are modeled at the crankshaft free end. This includes modeling both the crankshaft dynamical behavior and the excitation torques. As the engine is very large, the first crankshaft torsional modes are in the low frequency range. A model with the assumption of a flexible crankshaft is required. The excitation torques depend on the in-cylinder pressure curve. The latter is modeled with a phenomenological model. Mechanical and combustion parameters of the model are optimized with the help of actual data. Then, an automated diagnosis based on an artificially intelligent system is proposed. Neural networks are used for pattern recognition of the angular speed waveforms in normal and faulty conditions. Reference patterns required in the training phase are computed with the model, calibrated using a small number of actual measurements. Promising results are obtained. An experimental fuel leakage fault is successfully diagnosed, including detection and localization of the faulty cylinder, as well as the approximation of the fault severity.
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paper_title: On-line sensor fault detection, isolation, and accommodation in automotive engines

paper_content:

This paper describes the hybrid solution, based on artificial neural networks (ANNs), and the production rule adopted in the realization of an instrument fault detection, isolation, and accommodation scheme for automotive applications. Details on ANN architectures and training are given together with diagnostic and dynamic performance of the scheme.
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paper_title: Real-Time Implementation of IFDIA Scheme in Automotive Systems

paper_content:

The paper describes the implementation of an instrument fault detection, isolation, and accommodation (IFDIA) system developed for real-time automotive applications. The IFDIA architecture, which is based on artificial neural networks, was used to locate and accommodate faults that could occur in the main sensors involved in managing engine operation. Numerous online tests carried out in a variety of engine operating and vehicle drive conditions have confirmed the validity of the diagnostic procedure and its onboard applicability
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paper_title: Semi-physical Neural Network Model in Detecting Engine Transient Faults using the Local Approach

paper_content:

Abstract This paper investigates detection of an air leak fault in the intake manifold subsystem of an automotive engine during transient operation. Previously, it was shown that integrating the local approach with an auto-associative neural network model of the engine, significantly increased the sensitivity of fault detection. However, the drawback then is that the computational load is naturally dependent on the network complexity. This paper proposes the use of the available physical models to pre-process the original signals prior to model building for fault detection. This not only extracts existing relationships among the variables, but also helps in reducing the number of variables to be modelled and the related model complexity. The benefits of this improvement are demonstrated by practical application to a modern spark ignition 1.8 litre Nissan petrol engine.
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paper_title: Neural network fault classification of transient data in an automotive engine air path

paper_content:

Classification of automotive engine air path faults from transient data is investigated using Neural Networks (NNs). A generic Spark Ignition (SI) Mean Value Engine Model (MVEM) is used for experimentation. Several faults are considered, including sensor faults, Exhaust Gas Recycle (EGR) valve and leakage in intake manifold. Consideration of different fault intensities for all the sensor and component faults is a unique feature of this research. The identification of a fault and its intensity has been considered both equally important. Radial Basis Function (RBF) NNs are trained to detect and diagnose the faults, and also to indicate fault size, by recognising the different fault patterns occurring in the transient data. Three dynamic cases of fault occurrence are considered with increasing generality of engine operation: (1) engine accelerates or retards from mean speed, (2) engine runs at different steady speeds and (3) engine accelerates or retards from any initial speed. The approach successfully classifies the faults in each case.
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paper_title: Issues of Fault Diagnosis for Dynamic Systems

paper_content:

There is an increasing demand for dynamic systems to become safer, more reliable and more economical in operation. This requirement extends beyond the normally accepted safety-critical systems e.g., nuclear reactors, aircraft and many chemical processes, to systems such as autonomous vehicles and some process control systems where the system availability is vital. The field of fault diagnosis for dynamic systems (including fault detection and isolation) has become an important topic of research. Many applications of qualitative and quantitative modelling, statistical processing and neural networks are now being planned and developed in complex engineering systems. Issues of Fault Diagnosis for Dynamic Systems has been prepared by experts in fault detection and isolation (FDI) and fault diagnosis with wide ranging experience.Subjects featured include: - Real plant application studies; - Non-linear observer methods; - Robust approaches to FDI; - The use of parity equations; - Statistical process monitoring; - Qualitative modelling for diagnosis; - Parameter estimation approaches to FDI; - Fault diagnosis for descriptor systems; - FDI in inertial navigation; - Stuctured approaches to FDI; - Change detection methods; - Bio-medical studies. Researchers and industrial experts will appreciate the combination of practical issues and mathematical theory with many examples. Control engineers will profit from the application studies.
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paper_title: Signal processing and neural network toolbox and its application to failure diagnosis and prognosis

paper_content:

Many systems are comprised of components equipped with self-testing capability; however, if the system is complex involving feedback and the self-testing itself may occasionally be faulty, tracing faults to a single or multiple causes is difficult. Moreover, many sensors are incapable of reliable decision-making on their own. In such cases, a signal processing front-end that can match inference needs will be very helpful. The work is concerned with providing an object-oriented simulation environment for signal processing and neural network-based fault diagnosis and prognosis. In the toolbox, we implemented a wide range of spectral and statistical manipulation methods such as filters, harmonic analyzers, transient detectors, and multi-resolution decomposition to extract features for failure events from data collected by data sensors. Then we evaluated multiple learning paradigms for general classification, diagnosis and prognosis. The network models evaluated include Restricted Coulomb Energy (RCE) Neural Network, Learning Vector Quantization (LVQ), Decision Trees (C4.5), Fuzzy Adaptive Resonance Theory (FuzzyArtmap), Linear Discriminant Rule (LDR), Quadratic Discriminant Rule (QDR), Radial Basis Functions (RBF), Multiple Layer Perceptrons (MLP) and Single Layer Perceptrons (SLP). Validation techniques, such as N-fold cross-validation and bootstrap techniques, are employed for evaluating the robustness of network models. The trained networks are evaluated for their performance using test data on the basis of percent error rates obtained via cross-validation, time efficiency, generalization ability to unseen faults. Finally, the usage of neural networks for the prediction of residual life of turbine blades with thermal barrier coatings is described and the results are shown. The neural network toolbox has also been applied to fault diagnosis in mixed-signal circuits.
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paper_title: Neural network application to comprehensive engine diagnostics

paper_content:

The authors examine the application of trainable classification systems to the problem of diagnosing faults in engines at the manufacturing plant. It is demonstrated how a combination of conventional statistical processing methods and neural networks can be combined to create a classifier system for engine diagnostics. The most significant computational effort is required for the principal component analysis and to properly develop the hard-shell classifiers using data sets augmented with Monte Carlo methods. Once these procedures are carried out, the application of neural networks to the data set to obtain the trainable classifier is quite straightforward. >
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paper_title: Signal processing and neural network toolbox and its application to failure diagnosis and prognosis

paper_content:

Many systems are comprised of components equipped with self-testing capability; however, if the system is complex involving feedback and the self-testing itself may occasionally be faulty, tracing faults to a single or multiple causes is difficult. Moreover, many sensors are incapable of reliable decision-making on their own. In such cases, a signal processing front-end that can match inference needs will be very helpful. The work is concerned with providing an object-oriented simulation environment for signal processing and neural network-based fault diagnosis and prognosis. In the toolbox, we implemented a wide range of spectral and statistical manipulation methods such as filters, harmonic analyzers, transient detectors, and multi-resolution decomposition to extract features for failure events from data collected by data sensors. Then we evaluated multiple learning paradigms for general classification, diagnosis and prognosis. The network models evaluated include Restricted Coulomb Energy (RCE) Neural Network, Learning Vector Quantization (LVQ), Decision Trees (C4.5), Fuzzy Adaptive Resonance Theory (FuzzyArtmap), Linear Discriminant Rule (LDR), Quadratic Discriminant Rule (QDR), Radial Basis Functions (RBF), Multiple Layer Perceptrons (MLP) and Single Layer Perceptrons (SLP). Validation techniques, such as N-fold cross-validation and bootstrap techniques, are employed for evaluating the robustness of network models. The trained networks are evaluated for their performance using test data on the basis of percent error rates obtained via cross-validation, time efficiency, generalization ability to unseen faults. Finally, the usage of neural networks for the prediction of residual life of turbine blades with thermal barrier coatings is described and the results are shown. The neural network toolbox has also been applied to fault diagnosis in mixed-signal circuits.
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paper_title: An incremental neural learning framework and its application to vehicle diagnostics

paper_content:

This paper presents a framework for incremental neural learning (INL) that allows a base neural learning system to incrementally learn new knowledge from only new data without forgetting the existing knowledge. Upon subsequent encounters of new data examples, INL utilizes prior knowledge to direct its incremental learning. A number of critical issues are addressed including when to make the system learn new knowledge, how to learn new knowledge without forgetting existing knowledge, how to perform inference using both the existing and the newly learnt knowledge, and how to detect and deal with aged learnt systems. To validate the proposed INL framework, we use backpropagation (BP) as a base learner and a multi-layer neural network as a base intelligent system. INL has several advantages over existing incremental algorithms: it can be applied to a broad range of neural network systems beyond the BP trained neural networks; it retains the existing neural network structures and weights even during incremental learning; the neural network committees generated by INL do not interact with one another and each sees the same inputs and error signals at the same time; this limited communication makes the INL architecture attractive for parallel implementation. We have applied INL to two vehicle fault diagnostics problems: end-of-line test in auto assembly plants and onboard vehicle misfire detection. These experimental results demonstrate that the INL framework has the capability to successfully perform incremental learning from unbalanced and noisy data. In order to show the general capabilities of INL, we also applied INL to three general machine learning benchmark data sets. The INL systems showed good generalization capabilities in comparison with other well known machine learning algorithms.
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paper_title: Neuro-fuzzy-based fault detection of the air flow sensor of an idling gasoline engine

paper_content:

Abstract This paper presents a neuro-fuzzy-based diagnostic system for detecting the faults of an air flow sensor of an idling gasoline engine. Based on the Takagi-cSugeno fuzzy system model, the diagnostic system is formulated by the input-output relationships between symptoms and faults. The system parameters are regulated with the learning datum from experiments, using the steepest descent method and back-propagation algorithm. The proposed diagnostic system consists of two parts: one is to judge the fault of the sensor and the other is to identify the bias degree of the sensor. The experimental results show that the fault source and fault (bias) degree can be identified with the proposed diagnostic system, and indicate that the neuro-fuzzy strategy is an efficient and available approach for fault diagnosis problems of the gasoline engine system.
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paper_title: Fault detection in internal combustion engines using fuzzy logic

paper_content:

AbstractIn this study, a complementary fuzzy-logic-based fault diagnosis system was developed to diagnose the faults of an internal combustion engine (ICE) and the system incorporated with an engine test stand. The input variables of the fuzzy logic classifier were acquired via a data acquisition card and RS-232 port. The rule base of this system was developed by considering the theoretical knowledge, the expert knowledge, and the experiment results. The accuracy of the fuzzy logic classifier was tested by experimental studies which were performed under different fault conditions. Using the developed fault diagnosis system, ten general faults which were observed in the internal combustion engine were successfully diagnosed in real time. With these characteristics, the system could easily be used for fault diagnosis in test laboratories and in service workshops.
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paper_title: Fault detection and isolation for an experimental internal combustion engine via fuzzy identification

paper_content:

Certain engine faults can be detected and isolated by examining the pattern of deviations of engine signals from their nominal unfailed values. In this brief paper, we show how to construct a fuzzy identifier to estimate the engine signals necessary to calculate the deviation from nominal engine behavior, so that we may determine if the engine has certain actuator and sensor "calibration faults". We compare the fuzzy identifier to a nonlinear ARMAX technique and provide experimental results showing the effectiveness of our fuzzy identification based failure detection and identification strategy. >
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paper_title: Fault diagnosis in internal combustion engines using non-linear multivariate statistics

paper_content:

AbstractThis paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce non-linear relationships between the recorded engine variables, the paper proposes the use of non-linear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new non-linear variable reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady state operating conditions. More precisely, non-linear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause o...
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paper_title: Fault Diagnosis: Models, Artificial Intelligence, Applications

paper_content:

1. Introduction.- 2. Models in the diagnostics of processes.- 3. Process diagnostics methodology.- 4. Methods of signal analysis.- 5. Control theory methods in designing diagnostic systems.- 6. Optimal detection observers based on eigenstructure assignment.- 7. Robust H?-optimal synthesis of FDI systems.- 8. Evolutionary methods in designing diagnostic systems.- 9. Artificial neural networks in fault diagnosis.- 10. Parametric and neural network Wiener and Hammerstein models in fault detection and isolation.- 11. Application of fuzzy logic to diagnostics.- 12. Observers and genetic programming in the identification and fault diagnosis of non-linear dynamic systems.- 13. Genetic algorithms in the multi-objective optimisation of fault detection observers.- 14. Pattern recognition approach to fault diagnostics.- 15. Expert systems in technical diagnostics.- 16. Selected methods of knowledge engineering in systems diagnosis.- 17. Methods of acqusition of diagnostic knowledge.- 18. State monitoring algorithms for complex dynamic systems.- 19. Diagnostics of industrial processes in decentralised structures.- 20. Detection and isolation of manoeuvres in adaptive tracking filtering based on multiple model switching.- 21. Detecting and locating leaks in transmission pipelines.- 22. Models in the diagnostics of processes.- 23. Diagnostic systems.
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paper_title: Application of Dempster-Shafer theory in condition monitoring applications: a case study

paper_content:

Abstract This paper is concerned with the use of Dempster–Shafer theory in `fusion' classifiers. We argue that the use of predictive accuracy for basic probability assignments can improve the overall system performance when compared to `traditional' mass assignment techniques. We demonstrate the effectiveness of this approach in a case study involving the detection of static thermostatic valve faults in a diesel engine cooling system.
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paper_title: A Multi-Net System for the Fault Diagnosis of a Diesel Engine

paper_content:

A multi-net fault diagnosis system designed to provide an early warning of combustion-related faults in a diesel engine is presented. Two faults (a leaking exhaust valve and a leaking fuel injector nozzle) were physically induced (at separate times) in the engine. A pressure transducer was used to sense the in-cylinder pressure changes during engine cycles under both of these conditions, and during normal operation. Data corresponding to these measurements were used to train artificial neural nets to recognise the faults, and to discriminate between them and normal operation. Individually trained nets, some of which were trained on subtasks, were combined to form a multi-net system. The multi-net system is shown to be effective when compared with the performance of the component nets from which it was assembled. The system is also shown to outperform a decision-tree algorithm (C5.0), and a human expert; comparisons which show the complexity of the required discrimination. The results illustrate the improvements in performance that can come about from the effective use of both problem decomposition and redundancy in the construction of multi-net systems.
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paper_title: FAULT DETECTION AND IDENTIFICATION OF AUTOMOTIVE ENGINES USING NEURAL NETWORKS

paper_content:

Abstract Fault detection and isolation (FDI) in dynamic data from an automotive engine air path using artificial neural networks is investigated. A generic SI mean value engine model is used for experimentation. Several faults are considered, including leakage, EGR valve and sensor faults, with different fault intensities. RBF neural networks are trained to detect and diagnose the faults, and also to indicate fault size, by recognising the different fault patterns occurring in the dynamic data. Three dynamic cases of fault occurrence are considered with increasing generality of engine operation. The approach is shown to be successful in each case.
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paper_title: Radial basis function neural network in fault detection of automotive engines

paper_content:

Fault detection and isolation have become one of the most important aspects of automobile design. A fault detection (FD) scheme is developed for automotive engines in this paper. The method uses an independent Radial Basis Function (RBF) Neural Network model to model engine dynamics, and the modelling errors are used to form the basis for residual generation. A dependent RBFNN model is a model which uses output data of a plant as a target output then use it to train the neural network, while, The independent RBFNN model is a higher accuracy than the dependent model and the errors can be detected by this model, this is because this model does not dependent on the output of the plant and it will use its output as a target, so if any faults in the plant will be not effect in the model and this faults will be detected easily and clearly. The method is developed and the performance assessed using the engine benchmark, the Mean Value Engine Model (MVEM) with Matlab/Simulink. Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults considered are 10-20% change superimposed on the outputs of manifold pressure, temperature and crankshaft speed sensors; one component fault considered is air leakage in intake manifold and Exhaust Gas Recycle (EGR); the actuator fault considered is the malfunction of fuel injector. The simulation results showed that all the simulated faults can be clearly detected in the dynamic condition throughout the operating range. ::: ::: Keywords : Automotive engine, independent RBFNN model, RBF neural network, fault detection ::: ::: International Journal of Engineering, Science and Technology , Vol. 2, No. 10, 2010, pp. 1-8
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paper_title: Robust Model-Based Fault Diagnosis for Dynamic Systems

paper_content:

There is an increasing demand for dynamic systems to become safer and more reliable. This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital. It is clear that fault diagnosis is becoming an important subject in modern control theory and practice. Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework. It contains many important topics and methods; however, total coverage and completeness is not the primary concern. The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches. In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications. The first two chapters are of tutorial value and provide a starting point for newcomers to this field. The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications. This will certainly appeal to experts in this field. Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research. The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed. Although this is a research monograph, it will be an important text for postgraduate research students world-wide. The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world.
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paper_title: Trends in the application of model-based fault detection and diagnosis of technical processes

paper_content:

Abstract After a short overview of the historical development of model-based fault detection, some proposals for the terminology in the field of supervision, fault detection and diagnosis are stated, based on the work within the IFAC SAFEPROCESS Technical Committee. Some basic fault-detection and diagnosis methods are briefly considered. Then, an evaluation of publications during the last 5 years shows some trends in the application of model-based fault-detection and diagnosis methods.
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paper_title: Model-based fault detection and diagnosis: status and applications

paper_content:

Abstract For the improvement of reliability, safety and efficiency advanced methods of supervision, fault-detection and fault diagnosis become increasingly important for many technical processes. This holds especially for safety related processes like aircraft, trains, automobiles, power plants and chemical plants. The classical approaches are limit or trend checking of some measurable output variables. Because they do not give a deeper insight and usually do not allow a fault diagnosis, model-based methods of fault-detection were developed by using input and output signals and applying dynamic process models. These methods are based, e.g., on parameter estimation, parity equations or state observers. Also signal model approaches were developed. The goal is to generate several symptoms indicating the difference between nominal and faulty status. Based on different symptoms fault diagnosis procedures follow, determining the fault by applying classification or inference methods. This contribution gives a short introduction into the field and shows some applications for an actuator, a passenger car and a combustion engine.
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paper_title: Robust Model-Based Fault Diagnosis for Dynamic Systems

paper_content:

There is an increasing demand for dynamic systems to become safer and more reliable. This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital. It is clear that fault diagnosis is becoming an important subject in modern control theory and practice. Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework. It contains many important topics and methods; however, total coverage and completeness is not the primary concern. The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches. In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications. The first two chapters are of tutorial value and provide a starting point for newcomers to this field. The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications. This will certainly appeal to experts in this field. Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research. The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed. Although this is a research monograph, it will be an important text for postgraduate research students world-wide. The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world.
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paper_title: Application of model-based fault detection and diagnosis to the quality assurance of an automotive actuator

paper_content:

Abstract The increased degree of automation of technical processes requires an increased number of mechanical-electronic components. In order to maintain the dependability and availability despite of the increased complexity, new methods for the quality assurance of mechanical-electronic systems are necessary. This paper describes an automatic diagnostic system for an automotive actuator. Analytic models of the process under investigation are used in order to extract detailed information about the process, using only the usually measured signals and evaluating the signals e.g. by parameter estimation and state estimation. However, some relations, especially the cause — effect relations between the underlying faults and the observable symptoms, are quite difficult to represent by analytic models. A rule-based approach is more suitable to acquire, represent and process the diagnostic knowledge base. In order to cope with uncertainty, a fuzzy structure is applied to the classification of faults.
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paper_title: Intelligent model-based diagnostics for vehicle health management

paper_content:

The recent advances in sensor technology, remote communication and computational capabilities, and standardized hardware/software interfaces are creating a dramatic shift in the way the health of vehicles is monitored and managed. These advances facilitate remote monitoring, diagnosis and condition-based maintenance of automotive systems. With the increased sophistication of electronic control systems in vehicles, there is a concomitant increased difficulty in the identification of the malfunction phenomena. Consequently, the current rule-based diagnostic systems are difficult to develop, validate and maintain. New intelligent model-based diagnostic methodologies that exploit the advances in sensor, telecommunications, computing and software technologies are needed. In this paper, we will investigate hybrid model-based techniques that seamlessly employ quantitative (analytical) models and graph-based dependency models for intelligent diagnosis. Automotive engineers have found quantitative simulation (e.g. MATLAB/SIMULINK) to be a vital tool in the development of advanced control systems. The hybrid method exploits this capability to improve the diagnostic system's accuracy and consistency, utilizes existing validated knowledge on rule-based methods, enables remote diagnosis, and responds to the challenges of increased system complexity. The solution is generic and has the potential for application in a wide range of systems.© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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paper_title: Robust Model-Based Fault Diagnosis for Dynamic Systems

paper_content:

There is an increasing demand for dynamic systems to become safer and more reliable. This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital. It is clear that fault diagnosis is becoming an important subject in modern control theory and practice. Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework. It contains many important topics and methods; however, total coverage and completeness is not the primary concern. The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches. In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications. The first two chapters are of tutorial value and provide a starting point for newcomers to this field. The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications. This will certainly appeal to experts in this field. Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research. The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed. Although this is a research monograph, it will be an important text for postgraduate research students world-wide. The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world.
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paper_title: An iterative learning observer for fault detection and accommodation in nonlinear time-delay systems

paper_content:

This article addresses fault detection, estimation, and compensation problem in a class of disturbance driven time delay nonlinear systems. The proposed approach relies on an iterative learning observer (ILO) for fault detection, estimation, and compensation. When there are no faults in the system, the ILO supplies accurate disturbance estimation to the control system where the effect of disturbances on estimation error dynamics is attenuated. At the same time, the proposed ILO can detect sudden changes in the nonlinear system due to faults. As a result upon the detection of a fault, the same ILO is used to excite an adaptive control law in order to offset the effect of faults on the system. Further, the proposed ILO-based adaptive fault compensation strategy can handle multiple faults. The overall fault detection and compensation strategy proposed in the paper is finally demonstrated in simulation on an automotive engine example to illustrate the effectiveness of this approach. Copyright © 2005 John Wiley & Sons, Ltd.
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paper_title: Diagnosis of multiple sensor and actuator failures in automotive engines

paper_content:

Unlike cases where only a single failure occurs, fault detection and isolation of multiple sensor and actuator failures for engines are difficult to achieve because of the interactive effects of the failed components. If faults all appear either in sensors only or in actuators only, many existing residual generators which provide decoupled residual signals can be employed directly to obtain proper fault detection and isolation. However, when both sensor and actuator failures occur at the same time, their mutual effects on residuals make fault isolation particularly difficult. Under such circumstances, further decision logic is required. In the paper, the authors propose a hexadecimal decision table to relate all possible failure patterns to the residual code. The residual code is obtained through simple threshold testing of the residuals, which are the output of a general scheme of residual generators. The proposed diagnostic system incorporating the hexadecimal decision table has been successfully applied to automotive engine sensors and actuators in both simulation and experimental analyses. Enhancement of the present diagnostic performance by implementing an additional sensor is also described. >
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paper_title: Generating directional residuals with dynamic parity relations

paper_content:

Abstract It is shown how diagnostic residuals, which exhibit directional properties at all times in response to an arbitrary mix of input and output faults, can be generated using dynamic parity relations. The parity relations are applied directly to the observables of the monitored plant; the design relies on the plant's dynamic input-output model. This residual generator can be made computationally polynomial (moving average) by including the invariant zero polynomial of the fault system in the specified fault responses. The polynomial design yields moving average noise transfers as well. White noise transfer and disturbance decoupling are achieved by extending the response specification. The parity relation approach is compared with the traditional detection filter design, and is shown to be more straightforward and have milder existence conditions; if subjected to the same specification, the two approaches yield identical residual generators.
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paper_title: Diagnosis of automotive electronic throttle control systems

paper_content:

During the past two decades, the automotive industry has been required to develop on-board health monitoring capabilities to meet legislated diagnostic requirements for engine management systems. In this paper, real-time diagnostics are presented which monitor the performance of an electronic throttle control system to detect and identify a suite of anomalies. The ETC system shall be modeled and a parity diagnostic strategy applied to detect the presence of faults. The specific nature of the fault is isolated using a parametric estimation methodology. Representative numerical results are presented and discussed to demonstrate the operational performance of the ETC system and health monitoring algorithms.
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paper_title: Fault detection for modern Diesel engines using signal- and process model-based methods

paper_content:

Abstract Modern Diesel engines with direct fuel injection and turbo charging have shown a significant progress in fuel consumption, emissions and driveability. Together with exhaust gas recirculation and variable geometry turbochargers they became complicated and complex processes. Therefore, fault detection and diagnosis is not easily done and need to be improved. This contribution shows a systematic development of fault detection and diagnosis methods for two system components of Diesel engines, the intake system and the injection system together with the combustion process. By applying semiphysical dynamic process models, identification with special neural networks, signal models and parity equations residuals are generated. Detectable deflections of these residuals lead to symptoms which are the basis for the detection of several faults. Experiments with a 2.0 l Diesel engine on a dynamic test bench as well as in the vehicle have demonstrated the detection and diagnosis of several implemented faults in real time with reasonable calculation effort.
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paper_title: Nonlinear parity equation based residual generation for diagnosis of automotive engine faults

paper_content:

Abstract The parity equation residual generation method is a model-based fault detection and isolation scheme that has been applied with some success to the problem of monitoring the health of engineering systems. However, this scheme fails when applied to significantly nonlinear systems. This paper presents the application of a nonlinear parity equation residual generation scheme that uses forward and inverse dynamic models of nonlinear systems, to the problem of diagnosing sensor and actuator faults in an internal combustion engine, during execution of the United States Environmental Protection Agency Inspection and Maintenance 240 driving cycle. The Nonlinear AutoRegressive Moving Average Model with eXogenous inputs technique is used to identify the engine models required for residual generation. The proposed diagnostic scheme is validated experimentally and is shown to be sensitive to a number of input and sensor faults while remaining robust to the unmeasured load torque disturbance.
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paper_title: Fault detection and isolation using parity relations

paper_content:

Abstract The design of dynamic parity (consistency) relations, for the detection and isolation of faults, is described. Both additive and multiplicative (parametric) faults are considered. Various isolation schemes are discussed and decoupling from disturbances and certain model errors is included. Links to diagnostic observers and parameter estimation are pointed out.
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paper_title: Model Based Diagnosis for the Air Intake System of the SI-Engine

paper_content:

Because of legislative regulations like OBDII, on-board diagnosis has gained much interest lately. A model based approach is suggested for the diagnosis of the air intake system of an SI-engine. Im ...
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paper_title: Diagnosis and Prognosis of Automotive Systems: motivations, history and some results

paper_content:

Abstract The field of automotive engineering has seen an explosion in the presence of on-board electronic components and systems vehicles since the 1970s. This growth was initially motivated by the introduction of emissions regulations that led to the widespread application of electronic engine controls. A secondary but important consequence of these developments was the adoption of on-board diagnostics regulations aimed at insuring that emission control systems would operate as intended for a prescribed period of time (or vehicle mileage). In addition, the presence of micro-controllers on-board the vehicle has led to a proliferation of other functions related to safety and customer convenience, and implemented through electronic systems and related software, thus creating the need for more sophisticated on-board diagnostics. Today, a significant percentage of the software code in an automobile is devoted to diagnostic functions. This paper presents an overview of diagnostic needs and requirements in the automotive industry, illustrates some of the challenges that are associated with satisfying these requirements, and proposes some future directions.
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paper_title: IMPROVING DIAGNOSIS PERFORMANCES ON A TRUCK ENGINE MAKING USE OF STATISTICAL CHARTS

paper_content:

Abstract A method to improve model based diagnosis for the air path of a truck engine is presented. Originally, inaccuracies in both the volumetric efficiency static model and sensors limited the diagnosis performances. Statistical charts built from truck operational data were tested in order to reduce the overall residual dispersion. Data taken from two different trucks working in various operating conditions were used to evaluate the proposed approach. The use of charts is improving the diagnosis performances by approximately 30%.
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paper_title: Model-based diagnosis of an automotive engine using several types of fault models

paper_content:

Automotive engines is an important application for model-based diagnosis because of legislative regulations. A diagnosis system for the air-intake system of a turbo-charged engine is constructed. The design is made in a systematic way and follows a framework of hypothesis testing. Different types of sensor faults and leakages are considered. It is shown how many different types of fault models, e.g., additive and multiplicative faults, can be used within one common diagnosis system, and using the same underlying design principle. The diagnosis system is experimentally validated on a real engine using industry-standard dynamic test-cycles.
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paper_title: Decentralized Diagnosis in Heavy Duty Vehicles

paper_content:

Fault diagnosis is important for automotive vehicles, due to economic reasons, such as efficient repair and fault prevention, and legislative reasons, mainly safety and pollution. Embedded systems in vehicles includes a large number of electronic control units, that is connected to each other via an electronic network. Many of the current diagnostic systems use pre-compiled diagnostic tests, and fault-logs that stores the results from the tests. To improve the diagnostic system, fault localization in addition to the existing fault detection is wanted. Since there are limitations in processing power, memory, and network capacity, an algorithm is searched for that uses stated diagnoses in the control units to find the diagnoses for the complete system. Such an algorithm is presented and exemplified in the article. The embedded system used in a Scania heavy duty vehicle, has been used as a case study to find limitations in the embedded system, and realistic requirements on the algorithm. Copyright c © 2004 J. Biteus, M. Jensen, & M. Nyberg.
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paper_title: Determining the fault status of a component and its readiness, with a distributed automotive application

paper_content:

In systems using only single-component tests, the fault status of a component is ready if a test only supervising the component has been evaluated. However, if plausibility tests that supervise multiple components are used, then a component can be ready before all tests supervising the component have been evaluated. Based on test results, this paper contributes with conditions on when a component is ready. The conditions on readiness are given for both centralized and distributed systems and are here applied to the distributed diagnostic system in an automotive vehicle.
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paper_title: Injection diagnosis through common-rail pressure measurement

paper_content:

AbstractModern diesel common-rail injection systems supply fuel from a high-pressure vessel. The injection event causes an instantaneous drop in the rail pressure, as the stored mass is diminished. Pressure variations are also affected by the dynamics of the high-pressure pump that supplies fuel to the rail to compensate for the emptying process due to the injection. This paper proposes the possibility of diagnosing the injection process from measurement of the rail pressure. Different data treatment techniques are explored and evaluated in this paper to propose an effective method for the diagnosis of common-rail injection systems.
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paper_title: Model-based detection and isolation of faults due to ageing in the air and fuel paths of common-rail direct injection diesel engines equipped with a λ and a nitrogen oxides sensor

paper_content:

The air and fuel management of modern diesel engines is based on a feedforward control approach. As a consequence, faults of the air or the fuel path have a direct impact on the emissions that cannot be observed nor compensated. Faults due to the ageing of engine parts are very common; thus engines are designed with tolerances large enough to fulfil the emission standards for their entire lifetime. This paper presents a method to locate and quantify engine faults due to the ageing of parts of modern diesel engines in order to help to reduce design tolerances. A relative air-to-fuel ratio and a nitrogen oxides emission sensor have been applied to a test-bench engine, and control-oriented emission models have been developed. Based thereon, a model-based fault detection and isolation system has been implemented. The sensor signals are compared with the models in order to detect a possible fault, and fault location and size are estimated by means of a bank of extended Kalman filters, one for each fault considered. Results from the New European Driving Cycle show that the developed control-oriented models are accurate, and the faults can be correctly detected and isolated.
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paper_title: Model-based fault diagnosis of spark-ignition direct-injection engine using nonlinear estimations

paper_content:

In this paper, the detection and isolation of actuator faults (both measured and commanded) occurring in the engine breathing and the fueling systems of a spark-ignition direct-injection (SIDI) engine are described. The breathing system in an SIDI engine usually consists of a fresh air induction path via an electronically controlled throttle (ECT) and an exhaust gas recirculation (EGR) path via an EGR valve. They are dynamically coupled through the intake manifold to form a gas mixture, which eventually enters the engine cylinders for a subsequent combustion process. Meanwhile, the fueling system is equipped with a high-pressure common-rail injection for a precise control of the fuel quantity directly injected into the engine cylinders. Since the coupled system is highly nonlinear in nature, the fault diagnosis will be performed by generating residuals based on multiple nonlinear observers. Performing the fault detection and isolation properly on these key actuators in an SIDI engine could in principle ensure a precise control of air/fuel ratio and EGR dilution for improving fuel economy and reducing engine-out exhaust emissions.
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paper_title: Diesel fuel injection system faults diagnosis based on fuzzy injection pressure pattern recognition

paper_content:

On-line fuel injection system faults detection and diagnosis is a desirable function for diesel engines to ensure reliable operation. This paper introduces a fuzzy pattern recognition approach for assessing diesel fuel injection pressure patterns and detecting common fuel injection system faults based on the processed injection pressure data. A validation study indicated that this method was capable of screening out the characteristic parameters of the fuel injection system health condition. On-line diagnostic tests resulted in an over 80% of correct diagnoses on fuel injection system faults from 100 validation cases.
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paper_title: A Multi-Net System for the Fault Diagnosis of a Diesel Engine

paper_content:

A multi-net fault diagnosis system designed to provide an early warning of combustion-related faults in a diesel engine is presented. Two faults (a leaking exhaust valve and a leaking fuel injector nozzle) were physically induced (at separate times) in the engine. A pressure transducer was used to sense the in-cylinder pressure changes during engine cycles under both of these conditions, and during normal operation. Data corresponding to these measurements were used to train artificial neural nets to recognise the faults, and to discriminate between them and normal operation. Individually trained nets, some of which were trained on subtasks, were combined to form a multi-net system. The multi-net system is shown to be effective when compared with the performance of the component nets from which it was assembled. The system is also shown to outperform a decision-tree algorithm (C5.0), and a human expert; comparisons which show the complexity of the required discrimination. The results illustrate the improvements in performance that can come about from the effective use of both problem decomposition and redundancy in the construction of multi-net systems.
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paper_title: Detection of engine misfire by wavelet analysis of cylinder-head vibration signals.

paper_content:

The misfiring fault of internal combustion engine was detected by using the vibration signals of cylinder-head. Based on the data acquisition system built with LabVIEW, the cylinder-head vibration signals were detected with an accelerometer while the engine was rapidly accelerating from idle speed to high speed, at which time the engine was running under four working conditions of normal and single cylinder misfiring, double cylinders continuously misfiring and double cylinders alternately misfiring. After decomposing the vibration signals with db3 wavelet, whether the engine was misfiring or not, and what type of misfiring, were judged by comparing the decomposing results. The resultd showrf that, the low-frequency vibration of the engine cylinder head was related to the rotation of the principal shaft, and the high-frequency vibration was related to the combustion in the cylinder. There were certain corresponding relationships between wave crests of high-frequency vibration and wave crests of low-frequency under the four conditions of normal and faults when engine runs in idle segment, accelerating segment, and high-speed segment. Thus, the misfiring fault and type can be detected by analyzing the corresponding relations. Detection of the misfiring fault by using wavelet analysis was effective and feasible. Keywords: internal combustion engine, acceleration, multiple misfiring, wavelet analysis, LabVIEW DOI: 10.3965/j.issn.1934-6344.2008.02.001-007 Citation: Jiang Aihua, Li Xiaoyu, Huang Xiuchang, Zhang zhenhua, Hua Hongxing. Detection of engine misfire by wavelet analysis of cylinder-head vibration signals. Int J Agric & Biol Eng. 2008; 1(2): 1
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paper_title: Non-parametric models in the monitoring of engine performance and condition: Part 2: Non-intrusive estimation of diesel engine cylinder pressure and its use in fault detection

paper_content:

Abstract An application of the radial basis function model, described in Part 1, is demonstrated on a four-cylinder DI diesel engine with data from a wide range of speed and load settings. The prediction capabilities of the trained model are validated against measured data and an example is given of the application of this model to the detection of a slight fault in one of the cylinders.
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paper_title: Misfire-misfuel classification using support vector machines

paper_content:

AbstractThis paper proposes the use of support vector machines to perform classification between different types of missed combustion event in a six-cylinder engine. On-board diagnostics regulations require the detection of missed combustion events, which is possible through interpretation of crankshaft speed information. However, current approaches provide no information on the actual cause of the event, in particular whether it was caused by a misfuel (absence of fuel) or a misfire (absence of spark) event. Whilst the impact on the environment and emission treatment systems due to misfuel is minimal, misfire events are detrimental to both. Consequently information regarding the causes of missing combustion events potentially allows the development of unique recovery strategies particular to the source of the problem. In this paper, an approach is proposed that will provide the potential for, firstly, detection of a missing combustion event and, secondly, real-time classification of the event into either...
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paper_title: Misfire Detection Using a Neural Network Based Pattern Recognition Technique

paper_content:

This contribution investigates the practical application of artificial neural networks to misfire detection in gasoline engines.The problem of misfire detection is formulated as a pattern recognition problem. A feed-forward multiple-layer neural network is used for the classification of firing and misfiring events. Emphasis is given on the trade-off between performance, computational cost and implementabilitv of the technique on a production electronic control unit (ECL). The developed technique is applied to a six cylinder gasoline engine to detect misfire events over the whole range of operation defined by official on board diagnosis (OBD) regulations. Experimental results on a passenger car are presented.
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paper_title: Application of Nonlinear Sliding Mode Observers for Cylinder Pressure Reconstruction

paper_content:

Abstract The theoretical development of combining nonlinear sliding observers with internal combustion engine cycle simulations in order to estimate individual cylinder pressures was first presented by Kao & Moskwa in Ref. [8]. This new nonlinear approach to observe engine cylinder pressure, combustion heat release, and torque estimation has been shown to have good performance with fast convergence and stability. In this paper, the nonlinear sliding observer gains are modified to be a function of crank angle. It is applied to a six cylinder engine. The experimental results show the possibilities of application of this nonlinear observer to the cylinder pressure reconstruction.
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paper_title: Multiple Misfire Identification by a Wavelet-Based Analysis of Crankshaft Speed Fluctuation

paper_content:

The analysis of the crankshaft speed fluctuation is one of the most investigated and used techniques for the detection and isolation of the misfire events in an internal combustion engine. During the past, lots of methods for single misfire detection were presented in literature but the multiple misfire analysis still represents an open challenge. This paper describes a new analysis technique based on the wavelet approach that allows for both the extraction of the frequency components related to a misfire event, and its localization in the time domain. The detection process is performed by analyzing the crankshaft angular velocity measurement, in order to underline decelerations due to misfire events, and investigate the post-misfire pattern in a suitable domain (wavelet domain). Moreover, the proposed algorithm allows for an easy isolation of the cylinder responsible for the misfire. Experimental results will support the validity of the described approach.
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paper_title: Detection of misfire and knock in spark ignition engines by wavelet transform of engine block vibration signals

paper_content:

The high-frequency components of engine block vibration signals during misfire and normal combustion show clear differences of duration and scale by wavelet analysis - continuous wavelet transform (CWT). A new factor, combustion noise intensity (`CNICWT'), has been defined, which has been derived from the CWT scalogram. It has been shown that CNICWT can detect misfire more accurately than fast Fourier transform power spectral density. Similarly, it has been found that CNICWT can also be applied to knock detection. The durations of both knock and misfire detection overlapped each other at the same wavelet scale 8; misfire, normal and knock cycles can be indicated on the same histogram by CNICWT. Moreover, knock and misfire detection can be combined to one CNICWT computation.
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paper_title: Model-based engine fault detection using cylinder pressure estimates from nonlinear observers

paper_content:

There are two indicated torque input observers presented in paper. The input estimation problem is transformed into the control tracking problem. The sliding mode control with integrator and PI with feedforward control are used for tracking control blocks. The indicated torque estimations are applied to detect engine firing faults. Cylinder pressure centroid and cylinder pressure difference approaches for engine diagnostics are also discussed in this paper. Simulation results for a six-cylinder engine with abnormal combustion are plotted to show engine combustion problems. >
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paper_title: Estimate of indicated torque from crankshaft speed fluctuations: a model for the dynamics of the IC engine

paper_content:

A contribution is made to the task of constructing a global model for the IC (internal combustion) engine. A robust submodel is formulated for the dynamics of the IC engine, where-in the engine is viewed as a system with input given by cylinder pressure and output corresponding to crankshaft angular acceleration and crankshaft torque. The formulation is well suited to closed-loop engine control and transmission control applications. In the model, cylinder pressure is deterministically related to net engine torque through the geometry and dynamics of the reciprocating assembly. The relationship between net engine torque and crankshaft angular acceleration is explain in terms of a passive second-order electrical circuit model with constant parameters. Experimental results confirm the validity of the model over a wide range of engine operating conditions, including transient conditions. It is concluded that the model provides a powerful tool for estimating average and instantaneous net engine torque based on an inexpensive noncontacting measurement of crankshaft acceleration, thus providing access to one of the primary engine performance variables. >
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paper_title: The on-line detection of engine misfire at low speed using multiple feature fusion with fuzzy pattern recognition

paper_content:

This paper proposes a technique for the online detection of incipient engine misfire based on multiple feature fusion and fuzzy pattern recognition. The technique requires the measurement of instantaneous angular velocity signals. By processing the engine dynamics model equation in the angular frequency domain, four dimensionless features for misfire detection are defined, along with fast feature-extracting algorithms. By directly analysing the waveforms of the angular velocity and the angular acceleration, six other dimensionless features are extracted. Via fuzzy pattern recognition, all the features are associated together as a fuzzy vector. This vector identifies whether the engine is healthy or faulty and then locates the position of a misfiring cylinder or cylinders if necessary. The experimental work conducted on a production engine operating at low speeds confirms that such a technique is able to work with the redundant and complementary information of all the features and that it leads to improved diagnostic reliability. It is fully expected that this technique will be simple to implement and will provide a useful practical tool for the online monitoring and realtime diagnosis of engine misfire in individual cylinders.
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paper_title: Misfire and compression fault detection through the energy model

paper_content:

This article proposes a simple algorithm for misfire detection in reciprocating engines. The algorithm, based on an energy model of the engine, requires the measurement of the instantaneous angular speed. By processing the engine dynamics in the angular domain, variations in the working parameters of the engine, such as external load and mean angular speed, are compensated. A dimensionless feature has been abstracted for evaluation of the combustion as well as compression process of each cylinder. The proposed technique is expected to be easy to implement and to provide useful information for on-line monitoring of the in-cylinder processes in an internal combustion engine.
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paper_title: Residual generation and statistical pattern recognition for engine misfire diagnostics

paper_content:

Methods for diagnosing misfire in internal combustion engines are presented in this paper. Crank-angle domain digital filters are used to extract features from the measured engine speed signal that are characteristic of misfire. Features for intermittent and continuous misfires are developed separately, since the engine speed responses for intermittent and continuous misfires are distinctly different. Also, the influence of crankshaft torsional vibration and repeatable measurement errors must be addressed differently in each case. The outputs from the digital filters serve as inputs to a pattern recognition network based on linear parametric classifiers. Experimental results from implementation on a Ford 4.6L V-8 engine are provided.
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paper_title: Real-time misfire detection via sliding mode observer

paper_content:

A new method to detect misfire in internal combustion engines is presented. It is based on the estimation of the cylinder deviation torque by using sliding mode observer. The input estimation problem is transformed into the control tracking problem. The sliding controller is utilised to continuously track the measured varying crank speed by changing the estimated deviation torque. During the process of tracking, the speed estimation errors decrease and the gradual stability of the dynamics is assured. The mean deviation torque during the power stroke derived from the estimated deviation torque can be employed to detect easily engine misfires. Experimental results for a four-cylinder engine indicate that the method is a suitable tool for real-time misfire detection on board vehicle under various working conditions.
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paper_title: Real Time Estimation of Engine Torque for the Detection of Engine Misfires

paper_content:

The need for improvements in the one-line estimation of engine performance variables is greater bowadays as a result of more stringent emission control legislation. There is also a concurrent requirement for improved on-board diagnostics to detect different types of malfunctions. For example, recent California Air Resources Board (CARB) regulations mandate continuous monitoring of misfires, a problem which, short of an expensive measurement of combustion pressure in each cylinder, is most directly approached by estimating individual cylinder torque. This paper describes the theory and experimental results of a method for the estimation of individual cylinder torque in automative engines, with the intent of satisfying the CARB misfire detection requirements. Estimation, control, and diagnostic functions associated with automotive engines involve near periodic processes, due to the nature of multi-cylinder engines. Thze model of the engine dynamics used in this study fully exploits the inherent periodicity of the combustion process in the crank angle domain in order to obtain a simple deconvolution medhod for the estimation of the mean torque produced by each cylinder during each stroke from a measurement of crankshaft angular velocity. The deconvolution is actually performed in the spatial frequency domain, recognizing that the combustion energy is concentrated at discrete spatial frequencies, which are harmonics of the frequency of rotation of the crankshaft. Thus, the resulting deconvolution algorithm is independent of engine speed, and reduces to an algebraic operation in the frequency domain. It is necessary to perform a Discrete Fourier Transform (DFT) on the measured angular velocity signal, sampled at fixed uniform crank angle intervals. The paper discusses the model used in the study, and the experimental validations of the algorithm, which has been implemented in real time using a portable computer and has been tested extensively on different production vehicles on a chassis dynamometer and on the road
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paper_title: On-line estimation of indicated torque in IC engines via sliding mode observers

paper_content:

An approach to fault diagnosis for internal combustion engines is considered. It is based on the estimation of cylinder indicated torques by means of sliding mode observers. Instead of measuring indicated pressure in cylinders directly, crankshaft angular position is measured as the input of observers, which estimate the indicated torques. Several engine models are considered with different levels of complexity.
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paper_title: Cylinder pressure and combustion heat release estimation for SI engine diagnostics using nonlinear sliding observers

paper_content:

Cylinder pressure is an important parameter in engine combustion analysis or engine diagnosis. An approach is introduced to estimate cylinder pressure and combustion heat release in multicylinder SI engines based solely on engine speed measurements. Because of the nonlinear nature of engines, this estimation employs a nonlinear observer: the sliding observer. In many applications, cylinder pressure is critical for control or engine monitoring systems. Researchers have pursued various approaches to obtain the desired cylinder pressure directly or indirectly. However, these approaches vary in cost, reliability, robustness, accuracy and convenience. The use of nonlinear sliding observers in pressure and combustion heat release estimation based on measurements of engine speed provides an accurate, low-cost, and reliable way to acquire these desired states. In this paper the estimation of cylinder pressures and combustion heat releases of a multicylinder SI engine is presented. Since a problem of system observability arises in pressure estimation when the cylinder piston moves to its TDC, means of reducing estimation errors in this condition are described. Finally, the applications of this approach in engine diagnostics are discussed. >
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paper_title: An investigation of crankshaft oscillations for cylinder health diagnostics

paper_content:

The vibrational characteristics of an internal combustion engine crankshaft are investigated from a cylinder health diagnostics point of view. Experimental results from a six-cylinder industrial diesel engine are presented to demonstrate the effects of cylinder imbalance on the individual harmonic components of the engine speed signal. A crank-angle domain numerical model of the crankshaft dynamics for a six-cylinder industrial diesel engine is also adopted to establish the effects of continuous low-power production in individual cylinders of a multi-cylinder engine. Outline of a diagnostics algorithm that makes use of the properties of crankshaft vibration behaviour is provided. In particular, crank-angle domain notch filters are employed to extact the harmonic components of engine speed. The outlined method can be implemented for individual cylinder health diagnostics across a family of multi-cylinder engines and can be formulated to handle changes in crankshaft characteristics due to replacement of mechanical components and/or wear.
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paper_title: Engine misfire detection

paper_content:

Abstract In this paper a simplified engine model is introduced in order to estimate the combustion torque from angular velocity measurement, using a Kalman filter approach. It can be implemented on-board the vehicle. The proposed discrete state-space model works with angular rather than time-equidistant sampling. Signals from conventional angular speed sensors of available engine management systems are used, without additional hardware components. Errors introduced by the digital computation of the angular speed, as well as the mechanical imperfections of the target wheel, are also modelled and taken into account.
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paper_title: Knocking detection using wavelet instantaneous correlation method

paper_content:

Abstract In this study, we propose a new method for knocking detection that utilizes the vibration signal measured by a knock sensor under the knocking conditions known as a real mother wavelet (RMW), and carry out instantaneous correlation from the wavelet transform. We call this method the wavelet instantaneous correlation (WIC) method. The degree of similarity between the RMW and the vibration of the engine block was judged and only the knocking signal from the vibration of the engine block was extracted. The results obtained here show that the method proposed in this study is useful for knocking detection even if the engine speed is very high at 6000 rpm.
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paper_title: Detection of misfire and knock in spark ignition engines by wavelet transform of engine block vibration signals

paper_content:

The high-frequency components of engine block vibration signals during misfire and normal combustion show clear differences of duration and scale by wavelet analysis - continuous wavelet transform (CWT). A new factor, combustion noise intensity (`CNICWT'), has been defined, which has been derived from the CWT scalogram. It has been shown that CNICWT can detect misfire more accurately than fast Fourier transform power spectral density. Similarly, it has been found that CNICWT can also be applied to knock detection. The durations of both knock and misfire detection overlapped each other at the same wavelet scale 8; misfire, normal and knock cycles can be indicated on the same histogram by CNICWT. Moreover, knock and misfire detection can be combined to one CNICWT computation.
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paper_title: Engine Knock Detection from Vibration Signals Using Pattern Recognition

paper_content:

The paper deals with a diagnostic method that allows to detect engineknock. The developed algorithm differentiates three kinds of engine cycles:absence of knock, increasing knock and heavy knock. The decision is takenfrom a block vibration signal. The diagnostic method is based on patternrecognition. Three models of different data shapes provided from theaccelerometer are elaborated. This is done using a time-scale analysis toolcalled a wavelet network. It allows to extract relevant features from thesignal. The aim of the method is then to partition the feature space intoclasses representing the knock states. Experimental results are reported.
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paper_title: Knock detection in spark ignition engines by vibration analysis of cylinder block: A parametric modeling approach

paper_content:

A simple and novel method for detection of low intensity knock, occurred in spark ignition engines, is developed in this paper. The proposed method is based on the modeling of the cylinder block vibration signal by auto regressive moving average (ARMA) parametric model. It is observed that one of the estimated moving average parameters is highly sensitive to the knock, so by monitoring this parameter, it is possible to detect the knock in SI engines even in very initial stages. The results also demonstrate that the proposed method is capable of detecting knock by simple hardware with low sampling frequency, leads to reduction the computation time as well as hardware complexity and cost. Moreover, a new method of utilizing the tachometer signal in parallel to the accelerometer one to estimate the knock-sensitive window (KSW) is introduced.
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paper_title: Wavelet-based knock detection with fuzzy logic

paper_content:

This paper presents a novel approach to the determination of the knocking condition of a spark-ignition engine using the discrete wavelet transform as a means of analyzing the engine-block vibration signal and a fuzzy inference scheme to generate an estimate of the knock intensity. The block vibration sensor responds to knock-induced pressure waves generated when a portion of the air-fuel mixture in the cylinder combusts spontaneously. However, the block vibration signal is masked by other excitation signals, which render the deter- mination of the knocking condition of the combustion a difficult task. The wavelet transform lends itself very useful to the time-frequency analysis of the vibration signal. Various characteristic features can be determined from the wavelet decomposition of the signal. These are in turn fused together to generate an estimate of the knocking in- tensity using fuzzy logic. The proposed scheme was developed and tested using engine combustion data processed off-line. Off-line data was also used to optimize the parameters of the fuzzy scheme. The sys- tem was subsequently implemented on the real-time engine controller for on-line testing.
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paper_title: Mechanical signature analysis using time-frequency signal processing: application to internal combustion engine knock detection

paper_content:

Signature analysis consists of the extraction of information from measured signal patterns. The work presented in this paper illustrates the use of time-frequency (TF) analysis methods for the purpose of mechanical signature analysis. Mechanical signature analysis is a mature and developed field; however, TF analysis methods are relatively new to the field of mechanical signal processing, having mostly been developed in the present decade, and have not yet been applied to their full potential in this field of engineering applications. Some of the ongoing efforts are briefly reviewed in this paper. One important application of TF mechanical signature analysis is the diagnosis of faults in mechanical systems. In this paper we illustrate how the use of joint TF signal representations can result in tangible benefits when analyzing signatures generated by transient phenomena in mechanical systems, such as might be caused by faults or otherwise abnormal operation. This paper also explores signal detection concepts in the joint TF domain and presents their application to the detection of internal combustion engine knock.
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paper_title: Model-based fault diagnosis of spark-ignition direct-injection engine using nonlinear estimations

paper_content:

In this paper, the detection and isolation of actuator faults (both measured and commanded) occurring in the engine breathing and the fueling systems of a spark-ignition direct-injection (SIDI) engine are described. The breathing system in an SIDI engine usually consists of a fresh air induction path via an electronically controlled throttle (ECT) and an exhaust gas recirculation (EGR) path via an EGR valve. They are dynamically coupled through the intake manifold to form a gas mixture, which eventually enters the engine cylinders for a subsequent combustion process. Meanwhile, the fueling system is equipped with a high-pressure common-rail injection for a precise control of the fuel quantity directly injected into the engine cylinders. Since the coupled system is highly nonlinear in nature, the fault diagnosis will be performed by generating residuals based on multiple nonlinear observers. Performing the fault detection and isolation properly on these key actuators in an SIDI engine could in principle ensure a precise control of air/fuel ratio and EGR dilution for improving fuel economy and reducing engine-out exhaust emissions.
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paper_title: Real-Time Diagnosis of the Exhaust Recirculation in Diesel Engines Using Least-Squares Parameter Estimation

paper_content:

In this paper, we present a real-time parameter identification approach for diagnosing faults in the exhaust gas recirculation (EGR) system of Diesel engines. The proposed diagnostics method has the ability to detect and estimate the magnitude of a leak or a restriction in the EGR valve, which are common faults in the air handling system of a Diesel engine. Real-time diagnostics is achieved using a recursive-least-squares (RLS) method, as well as, a recursive formulation of a more robust version of the RLS method referred to as recursive total-least-squares method. The method is used to identify the coefficients in a static orifice flow model of the EGR valve. The proposed approach of fault detection is successfully applied to diagnose low-flow or high-flow faults in an engine and is validated using experimental data obtained from a Diesel engine test cell and a truck.
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paper_title: ROBUSTNESS ASSESSMENT OF ADAPTIVE FDI SYSTEM FOR ENGINE AIR PATH

paper_content:

Robustness assessment is important for every newly developed method. This paper presents robustness assessment of a new adaptive on-board fault diagnosis algorithm for the air-path of spark ignition (SI) engines. The method uses a radial basis function (RBF) neural network to classify pre-defined possible faults from engine measurements, reporting fault occurrence as well as the type and size of a fault. After diagnosing faults in each sample interval, the weights and widths of the RBF fault classifier are updated with the measurements and appropriately selected target outputs. Consequently, the network can adapt to the time-varying dynamics of the engine and environment change so that the false alarm rate is greatly reduced and the required network size is also reduced. The developed scheme is assessed with various faults simulated on a mean value engine benchmark model and compared with a fixed parameter RBF classifier. The robustness assessment is done for a wide range of operational modes for an automotive engine in real life. Special attention has been given to minimise the neural network size for its practical implementation in electronic control unit (ECU) of an automotive. Simulation results demonstrate the effectiveness of the proposed algorithm and its robustness.
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paper_title: Model-based diagnosis of an automotive engine using several types of fault models

paper_content:

Automotive engines is an important application for model-based diagnosis because of legislative regulations. A diagnosis system for the air-intake system of a turbo-charged engine is constructed. The design is made in a systematic way and follows a framework of hypothesis testing. Different types of sensor faults and leakages are considered. It is shown how many different types of fault models, e.g., additive and multiplicative faults, can be used within one common diagnosis system, and using the same underlying design principle. The diagnosis system is experimentally validated on a real engine using industry-standard dynamic test-cycles.
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paper_title: Loading and Regeneration Analysis of a Diesel Particulate Filter with a Radio Frequency-Based Sensor

paper_content:

Accurate knowledge of diesel particulate filter (DP F) loading is critical for robust and efficient ope ration of the combined engine-exhaust aftertreatment system. Furthermore, upcoming on-board diagnostics regulations require on-board technologies to evaluate the statu s of the DPF. This work describes the application of radio frequency (RF) ‐ based sensing techniques to accura tely measure DPF soot levels and the spatial distri bution of the accumulated material. A 1.9L GM turbo diesel e ngine and a DPF with an RF-sensor were studied. Direct comparisons between the RF measurement and conventional pressure-based methods were made. Further analysis of the particulate matter loading rates wa s obtained with a mass-based soot emission measurement instrument (TEOM). Comparison with pressure drop measurements show the RF technique is unaffected by exhaust flow variati ons and exhibits a high degree of sensitivity to DPF so ot loading and good dynamic response. Additional computational and experimental work further illustr ates the spatial resolution of the RF measurements. Based on the experimental results, the RF technique shows significant promise for improving DPF control enab ling optimization of the combined engine-aftertreatment system for improved fuel economy and extended DPF service life.
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paper_title: Diesel Particulate Filter Diagnostics Using Correlation and Spectral Analysis

paper_content:

Surve, Pranati Ramdas. M.S.E.C.E., Purdue University, August 2008. Diesel Particulate Filter Diagnostics Using Correlation and Spectral Analysis. Major Professors: Peter H. Meckl and Venkataramanan Balakrishnan. Diesel Particulate Filters (DPF) are used to trap the harmful particulate matter (PM) present in the exhaust of diesel engines. The particulate matter is trapped in and on a porous ceramic substrate to keep PM emissions low. The onboard diagnostics requirements enforced by Environmental Protection Agency (EPA) require that the DPF perform well to keep emissions below certain specified levels. Further, should the DPF fail in any way, resulting in higher emission levels, this event must be detected by the engine control module. The objective of this work is to “detect failed DPF condition”. The temperature and pressure signals from transducers inserted into the inlet and outlet of the DPF are analyzed. The approach is to correlate the pre-DPF and post-DPF temperature and pressure signals and define the transfer function characteristics for nominal DPF behavior. Determining how these characteristics change as a result of filter failure forms the basis of a DPF fault detection algorithm. It is observed from the test data that for the pressure signal, other than the mean value signal (i.e., at zero frequency), most of the energy content is concentrated at the firing frequency of the engine. The dynamic pressure signals are used to determine the magnitude squared of the transfer function characteristics of DPF by energy spectral analysis. This approach can achieve a failure detection of lightly failed DPF which is not possible by current algorithms based on mean value pressure drop. The most significant contribution of this research is the extension of dynamic pressure signal analysis from steady-state engine operation to transient operating conditions.
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paper_title: Lean NOx trap storage model for diesel engine aftertreatment control and diagnosis

paper_content:

NOx emissions are a great concern to the environment. One major NOx emission source is from off-road and highway vehicles powered with diesel engines. Diesel engines offer significant benefits in fuel economy and low-speed torque performance. However, these benefits come at the expense of increased aftertreatment complexity. A lean NOx trap (LNT) is one effective way to reduce emissions to meet strict EPA regulations. Based on the chemical kinetics analysis of the LNT adsorption mechanism and mass transfer process, a storage model was proposed to estimate the mass of NOx adsorbed inside an LNT during lean periods based on other available signals. The model includes exhaust gas mass flow rate, in-bed temperature, inflow NOx concentrations, and NOx adsorption capacity of the LNT. Model validation shows close agreement between predictions and experimental data. This storage model can be used for LNT regeneration control and fault detection.
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paper_title: Model-based fault detection and isolation for a diesel lean NOx trap aftertreatment system

paper_content:

Abstract The lean NO x trap (LNT) is an aftertreatment device used to reduce nitrogen oxides emissions on Diesel engines. To operate the LNT with high conversion efficiency, an optimized regeneration schedule is required, together with closed-loop control of the air/fuel ratio during regeneration. Furthermore, to comply with emissions regulations, diagnostic schemes are needed to detect and isolate faults, typically related to aging, sulfur poisoning and thermal deactivation. The paper describes a step-by-step methodology for the design and validation of model-based fault diagnosis for a LNT aftertreatment system. The approach is based on a control-oriented model of the LNT validated with experimental data. The proposed diagnostic approach is based on the generation of residuals using system models, through the comparison of the predicted and measured values of selected output variables. The paper focuses on the detection and isolation of sensor faults and LNT parametric faults. Different diagnostic methodologies are presented in relation to the detection of specific faults. Starting from sulfur poisoning detection in a laboratory environment which represents a preliminary validation of the approach, the diagnostic scheme is extended to detect various faults under different plant configurations and operating conditions, with a final application to on-board fault detection and isolation.
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paper_title: A Methodology for Fault Diagnosis of Diesel NOx Aftertreatment Systems

paper_content:

Abstract Diesel engines are today considered leading candidates for the new generations of passenger vehicles due to their fuel efficiency and drivability. One of the key elements for the future acceptability is the compliance with emission standards (particularly on nitrogen oxides), which will require precise control of the aftertreatment system. Furthermore, in light of OBD-II regulations, considerable research must be devoted to the design of fault diagnosis algorithms. The definition of fault diagnosis strategies is a complex process that involves thorough studies of the system behavior in healthy and faulty conditions. Such studies can be done in multiple ways, including experimentation and mathematical modeling. In both cases, a thorough knowledge of the system components, sensors and actuators is required. The proposed paper presents an approach to model-based fault diagnosis of Diesel NOx aftertreatment systems. The proposed methodology is based on a functional and structural analysis of the system, at the level of individual components and assemblies. This facilitates the mapping and characterization of system faults through FTA and FMEA methods, allowing for the design of control-oriented models to be used for fault detection and isolation. In this paper, the outlined approach is applied to a Lean NOx Trap system.
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paper_title: NOX storage and reduction on a Pt/BaO/alumina monolithic storage catalyst

paper_content:

Abstract The performance of a Pt/BaO/Al2O3 washcoated monolith reactor is investigated using propylene as a reductant. The dependence of the time-averaged NOX conversion is reported for several operating parameters, including feed composition, temperature, flow rate, propylene pulse duration and overall cycle time. NOX storage data, which reveal both kinetic and thermodynamic limitations, provide guidance on selecting the feed protocol giving high NOX conversion. Complex nonisothermal transient features spanning the NOX storage and reduction regimes are revealed. Time-averaged NOX conversions exceeding 80% are achieved over a wide range of feed temperatures when short pulses are fed that have a sufficiently high propylene concentration to create fuel-rich conditions. The periodic feed of propylene gives time-averaged NOX conversion significantly exceeding the steady-state conversion for an equivalent propylene feed rate. The results indicate that the rich–lean feed protocol must be tuned in order to achieve a maximum NOX conversion with minimal breakthrough of incompletely oxidized components. The time-averaged conversion achieves a maximum at an intermediate cycle time and reductant pulse duty, with values dependent on the feed temperature and flow rate. The results are compared to previous literature data and interpreted with a phenomenological storage and reduction cycle.
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paper_title: An extended Kalman filter for ammonia coverage ratio and capacity estimations in the application of Diesel engine SCR control and onboard diagnosis

paper_content:

Ammonia, as the reductant to convert NOx to nitrogen molecules in a selective catalytic reduction (SCR) catalyst, plays an important role in Diesel engine SCR control. To achieve a sophisticated SCR control, the presence of ammonia in the SCR should be precisely monitored. Ammonia sensor for gaseous concentration measurement has been available recently. However, ammonia can also be adsorbed on the SCR substrate which is unmeasurable but dominates the NOx reduction dynamics. And also the catalyst ammonia storage capacity serves as an important factor for the judgment of SCR catalyst aging. In this paper, an observer for SCR catalyst ammonia coverage ratio and storage capacity estimations is designed based on an extended Kalman filter (EKF). Simulation results verify that the EKF-based observer can estimate both the states well.
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paper_title: Observer-based estimation of selective catalytic reduction catalyst ammonia storage

paper_content:

AbstractThis paper presents two non-linear observer designs with different robustness to estimate engine selective catalytic reduction (SCR) ammonia coverage ratio. The observers estimate the SCR catalyst ammonia storage (or coverage ratio) based on the available measurements of NOx, NH3, and temperature. An extended Kalman filter is proposed to eliminate the NOx sensor cross-sensitivity to ammonia. A sliding mode observer technique was employed for the design. The robustness of the observers with respect to sensor measurement uncertainties was theoretically analysed. FTP75 test cycle simulation results show the robustness and estimation accuracies of the different observers in the presence of different measurement errors/uncertainties.
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paper_title: On‐board diagnosis for three‐way catalytic converters

paper_content:

On-board fault diagnosis is critical for the automotive industry. Recently, new on-board diagnostic system requirements (OBD II) have been enforced on California vehicles and new legislation will become stricter and stricter; moreover such requirements have also been extended in Europe (EOBD). Government regulations will require monitoring vehicle emissions and alerting the driver if the exhaust after-treatment system is not working properly. To meet these requirement, sophisticated diagnostic algorithms have to be developed. This paper presents a model-based stochastic approach for fault detection with application to automotive exhaust-gas after-treatment systems. The algorithm, based on relatively simple control-oriented models of the three-way catalytic converter and the oxygen sensor, is suitable for real-time, on-board applications. The overall strategy has been tuned and validated on the basis of experimental data. Copyright © 2001 John Wiley & Sons, Ltd.
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paper_title: Data Driven Simplified Three-Way Catalyst Health Diagnostic Models: Experimental Results

paper_content:

Presented is the identification of simplified three-way catalyst (TWC) models from vehicle data. The simplified models are developed for multiple TWCs from two different classes: full useful life and threshold. The full useful life (FUL) TWCs represent catalysts from vehicles with 100,000 miles whereas threshold TWCs represent catalysts from vehicles with over 150,000 miles. The results showed that these simplified models have consistent parameter estimates when identified in a passive monitoring mode as the vehicle experiences a driving cycle from the Federal Test Procedure (FTP). Moreover the parameters of the simplified TWC models contain TWC health/age information. The model input is the air mass flow rate (AM) into the engine and the model output is a proposed TWC health measure developed in this paper. The impact of TWC temperature will also be detailed.Copyright © 2007 by ASME
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paper_title: A model-based approach to automotive three-way catalyst on-board monitoring☆

paper_content:

Abstract A model-based three-way automotive catalyst monitoring and fault detection strategy is presented in this work. The performance of the catalyst is inferred from the error between the post-catalyst exhaust gas oxygen sensor air fuel ratio measurement and the model predicted value. A simplified catalyst oxygen storage and reversible deactivation model is employed to predict the post-catalyst air fuel ratio. The model-based strategy is based on the use of a test statistic that is computed from a window of post-catalyst air fuel ratio prediction error data updated in real-time. A fault is assumed to be present when the value of this statistic exceeds a threshold determined from some specified confidence level. We illustrate several test statistics using engine operating data and conclude with an evaluation of this strategy.
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paper_title: Model-Based Control and Diagnostic Monitoring for Automotive Catalyst Systems

paper_content:

An integrated model-based automotive catalyst control and diagnostic monitoring system is presented. This system incorporates a simplified dynamic catalyst model that describes oxygen storage and release in the catalyst and predicts the post-catalyst exhaust gas oxygen sensor signal. The model-based controller maintains the catalyst stored oxygen at a desired operating condition to prevent post-catalyst emissions breakthrough by ultimately adjusting the engine fueling. Diagnostic monitoring is performed by detecting changes in a test statistic derived from the post-catalyst sensor response. These changes, which are sensitive to both long-term catalyst deactivation effects and short-term emission control device failures, are detected based on the result of nonparametric statistical tests on a moving horizon of past test statistic values.
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paper_title: Three-way catalyst diagnostics for advanced emissions control systems

paper_content:

Automotive emissions are stringently regulated. Since 1980, a three-way catalyst (TWC) has been used to convert harmful emissions of hydrocarbons, carbon monoxide, and oxides of nitrogen into less harmful gases in order to meet these regulations. The TWC's efficiency of conversion of these gases is primarily dependent on the mass ratio of air to fuel (A./F) in the mixture leaving the exhaust manifold and entering the catalyst. This paper develops a method by which a dynamic TWC model can be used for diagnostic purposes. This diagnostic method is analyzed in the context of a hypothesis test that is based on the oxygen storage capacity of the TWC. The Neyman-Pearson criterion is used as the basis for this hypothesis test. It is initially applied in the case of a single sample where the variance of the data is assumed to be known. This is then expanded to a multiple-sample case through the use of Student's t test. The improved fidelity of the t test is demonstrated, and it is shown that larger sample sizes provide further improvement in the quality of the hypothesis test.
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paper_title: Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines

paper_content:

A fault signal diagnosis technique for internal combustion engines that uses a continuous wavelet transform algorithm is presented in this paper. The use of mechanical vibration and acoustic emission signals for fault diagnosis in rotating machinery has grown significantly due to advances in the progress of digital signal processing algorithms and implementation techniques. The conventional diagnosis technology using acoustic and vibration signals already exists in the form of techniques applying the time and frequency domain of signals, and analyzing the difference of signals in the spectrum. Unfortunately, in some applications the performance is limited, such as when a smearing problem arises at various rates of engine revolution, or when the signals caused by a damaged element are buried in broadband background noise. In the present study, a continuous wavelet transform technique for the fault signal diagnosis is proposed. In the experimental work, the proposed continuous wavelet algorithm was used for fault signal diagnosis in an internal combustion engine and its cooling system. The experimental results indicated that the proposed continuous wavelet transform technique is effective in fault signal diagnosis for both experimental cases. Furthermore, a characteristic analysis and experimental comparison of the vibration signal and acoustic emission signal analysis with the proposed algorithm are also presented in this report.
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paper_title: Fault correction of an airflow signal in a gasoline engine system using a neural fuzzy scheme and genetic algorithm

paper_content:

AbstractThis study presents an airflow correction system for correcting the fault of an airflow signal of a gasoline engine. The correction system consists of a fuzzy-model-based airflow estimation system and an airflow-monitoring system. The airflow estimation system estimates the normal airflow based on fuzzy neural networks and trained using the steepest-descent method and back-propagation algorithm. The airflow-monitoring system corrects the fault airflow signal according to the correction law. In order to raise the training performance, a genetic algorithm is used to search the learning rates and initial consequent gains of the fuzzy neural networks. The estimated results indicate that using a genetic algorithm certainly improves the performance of normal airflow estimation. The corrected results indicate that the airflow correction system can provide a feasible means of carrying out airflow fault correction in gasoline engine systems.
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paper_title: MODEL-BASED ON-BOARD FAULT DETECTION AND DIAGNOSIS FOR AUTOMOTIVE ENGINES

paper_content:

This paper desribes an algorithm developed for the online detection and diagnosis of faults in automobile engine sensors and actuators, using the on-board microcomputer. The algorithm is based on the structured parity equation methodology. The parity equations are derived from an engine model having linear dynamics and static nonlinearities, obtained by identification from simualtion experiments.
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paper_title: Neuro-fuzzy-based fault detection of the air flow sensor of an idling gasoline engine

paper_content:

Abstract This paper presents a neuro-fuzzy-based diagnostic system for detecting the faults of an air flow sensor of an idling gasoline engine. Based on the Takagi-cSugeno fuzzy system model, the diagnostic system is formulated by the input-output relationships between symptoms and faults. The system parameters are regulated with the learning datum from experiments, using the steepest descent method and back-propagation algorithm. The proposed diagnostic system consists of two parts: one is to judge the fault of the sensor and the other is to identify the bias degree of the sensor. The experimental results show that the fault source and fault (bias) degree can be identified with the proposed diagnostic system, and indicate that the neuro-fuzzy strategy is an efficient and available approach for fault diagnosis problems of the gasoline engine system.
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paper_title: Developing a fault tolerant power‐train control system by integrating design of control and diagnostics

paper_content:

Fault detection, isolation and fault tolerant control are investigated for an spark ignition engine. Fault tolerant control refers to a strategy in which the desired stability and robustness of the control system are guaranteed in the presence of faults. In an attempt to realize fault tolerant control, a methodology for integrated design of control and fault diagnostics is proposed. Specifically, the integrated design of control and diagnostics is achieved by combining the integral sliding mode control methodology and observers with hypothesis testing. Information obtained from integral sliding mode control and from observers with hypothesis testing is utilized so that a fault can be detected, isolated and compensated. As an application example, the air and fuel dynamics of an IC engine are considered. A mean value engine model is developed and implemented in Simulink®. The air and fuel dynamics of the engine are identified using experimental data. The proposed algorithm for integration of control and diagnostics is then validated using the identified engine model. Copyright © 2001 John Wiley & Sons, Ltd.
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paper_title: Improved SI engine modelling techniques with application to fault detection

paper_content:

This paper applies a fault detection strategy to a Jaguar car engine. Real data, over several engine speeds, is used to obtain an improved model of a subsystem comprising of the air intake, the manifold dynamics and the engine pumping. Modelling of an air leak is also considered. Three practical fault scenarios are assessed, deemed important by the manufacturer. A nonlinear observer method is used to detect practical sensor faults and an air leak in the manifold. Isolation logic is designed with respect to a restricted set of sensor measurements. The effectiveness of both modelling and fault detection axe assessed. A practical aim is to limit engine pollutants caused by system faults.
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paper_title: Diagnosis of sensor bias faults

paper_content:

Describes a robust sensor fault diagnosis algorithm for a class of nonlinear dynamic systems. Specifically, the paper uses adaptive techniques to estimate the unknown constant sensor bias in the presence of system modeling uncertainties and sensor noise. The robustness, sensitivity and stability of the adaptive fault diagnosis architecture are rigorously established. A simulation example to illustrate the use of the proposed fault diagnosis architecture to diagnose bias in an automotive Universal Exhaust Gas Oxygen sensor is presented.
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paper_title: Automotive engine diagnosis and control via nonlinear estimation

paper_content:

The aim of this article is to explore a possible approach to the problem of designing control and diagnostic strategies for future generations of automotive engines. The work described focuses on the use of physical models to estimate unmeasured or unmeasurable variables and parameters, to be used for control and diagnostic purposes. We introduce the mean value engine model used, present a conceptual strategy for combined control and diagnosis focusing mainly on the problem of air fuel ratio control, review basic concepts related to estimation in nonlinear systems, and propose various forms of the estimators that serve different objectives. We illustrate estimation problems in the context of a simplified engine model, assuming both linear and nonlinear measurement of oxygen concentration in the exhaust. Some simulation and experimental results related to estimation-based control and diagnosis are shown.
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paper_title: Diagnosis of an automotive emission control system using fuzzy inference

paper_content:

Abstract Fault diagnosis of a physical plant is crucial for its healthy performance, as it could ultimately prevent catastrophic failure, help comply with environmental regulations, and enhance customer satisfaction. There exist several methods to detect and isolate incipient faults that might cause a plant’s performance to deviate from the nominal, which can be either subjective or objective. A scheme and methodology for integrating subjective (heuristic) and objective (analytical) knowledge for fault diagnosis and decision-making using fuzzy logic is demonstrated in this paper. Furthermore, the structure, challenges, and benefits of such integration are explored. Also, experimental results of the work carried out are presented.
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paper_title: Nonlinear parity equation based residual generation for diagnosis of automotive engine faults

paper_content:

Abstract The parity equation residual generation method is a model-based fault detection and isolation scheme that has been applied with some success to the problem of monitoring the health of engineering systems. However, this scheme fails when applied to significantly nonlinear systems. This paper presents the application of a nonlinear parity equation residual generation scheme that uses forward and inverse dynamic models of nonlinear systems, to the problem of diagnosing sensor and actuator faults in an internal combustion engine, during execution of the United States Environmental Protection Agency Inspection and Maintenance 240 driving cycle. The Nonlinear AutoRegressive Moving Average Model with eXogenous inputs technique is used to identify the engine models required for residual generation. The proposed diagnostic scheme is validated experimentally and is shown to be sensitive to a number of input and sensor faults while remaining robust to the unmeasured load torque disturbance.
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paper_title: Intake Air Path Diagnostics for Internal Combustion Engines

paper_content:

Presented is the detection, isolation, and estimation of faults that occur in the intake air path of internal combustion engines during steady state operation. The proposed diagnostic approach is based on a static air path model, which is adapted online such that the model output matches the measured output during steady state conditions. The resulting changes in the model coefficients create a vector whose magnitude and direction are used for fault detection and isolation. Fault estimation is realized by analyzing the residual between the actual sensor measurement and the output of the original (i.e., healthy) model. To identify the structure of the steady state air path model a process called system probing is developed. The proposed diagnostics algorithm is experimentally validated on the intake air path of a Ford 4.6 L V-8 engine. The specific faults to be identified include two of the most problematic faults that degrade the performance of transient fueling controllers: bias in the mass air flow sensor and a leak in the intake manifold. The selected model inputs include throttle position and engine speed, and the output is the mass air flow sensor measurement.
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paper_title: Detection of sensor failures in automotive engines

paper_content:

The real-time application of detection filters to the diagnosis of sensor failures in automotive engine control systems is presented. The detection filter is the embodiment of a model-based failure detection and isolation (FDI) methodology, which utilizes analytical redundancy within a dynamical system to isolate the cause and location of abnormal behavior. The philosophy and essential features of FDI theory are presented, and the practical application of the method to the diagnosis of faults in some key sensors in an electronically controlled internal combustion engine is described. The experimental results presented here have been obtained on a production vehicle, and demonstrate that the real-time implementation of such detection filters is feasible, opening the way to a new generation of diagnostic strategies. >
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paper_title: Fault detection in internal combustion engines using fuzzy logic

paper_content:

AbstractIn this study, a complementary fuzzy-logic-based fault diagnosis system was developed to diagnose the faults of an internal combustion engine (ICE) and the system incorporated with an engine test stand. The input variables of the fuzzy logic classifier were acquired via a data acquisition card and RS-232 port. The rule base of this system was developed by considering the theoretical knowledge, the expert knowledge, and the experiment results. The accuracy of the fuzzy logic classifier was tested by experimental studies which were performed under different fault conditions. Using the developed fault diagnosis system, ten general faults which were observed in the internal combustion engine were successfully diagnosed in real time. With these characteristics, the system could easily be used for fault diagnosis in test laboratories and in service workshops.
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paper_title: Exhaust pressure estimation and its application to variable geometry turbine and wastegate diagnostics

paper_content:

Exhaust pressure is a critical engine parameter used to calculate engine volumetric efficiency and EGR flow rate. In this paper, exhaust pressure is estimated for an internal combustion engine equipped with a variable geometry turbocharger. A coordinate transformation is applied to generate a turbine map for estimation of the exhaust pressure. This estimation can be used to replace an expensive pressure sensor for cost saving. On the other hand, for internal combustion engines that have already installed exhaust pressure sensors, this estimation can be used to generate residual signals for model-based diagnostics. Based on the residual signals, two diagnostic methods are proposed: one based on cumulative sum algorithms and the other based on pattern recognition and neural networks. The algorithms are able to detect and isolate different failure modes for a turbocharger system.
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paper_title: Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results

paper_content:

Abstract The paper reviews the state of the art of fault detection and isolation in automatic processes using analytical redundancy, and presents some new results. It outlines the principles and most important techniques of model-based residual generation using parameter identification and state estimation methods with emphasis upon the latest attempts to achieve robustness with respect to modelling errors. A solution to the fundamental problem of robust fault detection, providing the maximum achievable robustness by decoupling the effects of faults from each other and from the effects of modelling errors, is given. This approach not only completes the theory but is also of great importance for practical applications. For the case where the prerequisites for complete decoupling are not given, two approximate solutions—one in the time domain and one in the frequency domain—are presented, and the crossconnections to earlier approaches are evidenced. The resulting observer schemes for robust instrument fault detection, component fault detection, and actuator fault detection are briefly discussed. Finally, the basic scheme of fault diagnosis using a combination of analytical and knowledge-based redundancy is outlined.
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paper_title: Application of an Effective Data-Driven Approach to Real-time time Fault Diagnosis in Automotive Engines

paper_content:

A dominant thrust in modern automotive industry is the development of "smart service systems" for the comfort of customers. The current on-board diagnosis systems embedded in the automobiles follow conventional rule-based diagnosis procedures, and may benefit from the introduction of sophisticated artificial intelligence and pattern recognition-based procedures in terms of diagnostic accuracy. Here, we present a mode-invariant fault diagnosis procedure that is based on data -driven approach, and show its applicability to automotive engines. The proposed approach achieves high-diagnostic accuracy by detecting the faults as soon as they occur. It uses statistical hypothesis tests to detect faults, a wavelet-based preprocessing of the data, and pattern recognition techniques for classifying various faults in engines. We simulate the Toyota Camry 544N Engine SIMULINK model in a real-time simulator and controlled by a prototype ECU (Electronic Control Unit). The engine model is simulated under several operating conditions (pedal angle, engine speed, etc), and pre-and post-fault data is collected for eight engine faults with different severity levels, and a database of cases is created for applying the presented approach. The results demonstrate that appealing diagnostic accuracy and fault severity estimation are possible with pattern recognition-based techniques, and, in particular, with the support vector machines.
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paper_title: Fault detection and isolation for an experimental internal combustion engine via fuzzy identification

paper_content:

Certain engine faults can be detected and isolated by examining the pattern of deviations of engine signals from their nominal unfailed values. In this brief paper, we show how to construct a fuzzy identifier to estimate the engine signals necessary to calculate the deviation from nominal engine behavior, so that we may determine if the engine has certain actuator and sensor "calibration faults". We compare the fuzzy identifier to a nonlinear ARMAX technique and provide experimental results showing the effectiveness of our fuzzy identification based failure detection and identification strategy. >
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paper_title: Use of Neural Networks for Modelling and Fault Detection for the Intake Manifold of a SI Engine

paper_content:

A Jaguar Car engine is used to provide data for modelling the throttle body, engine pumping and manifold body. Based on the gas law of the intake dynamics, input/output variables are identified and used to train a neural network. Various structures are compared and assessed. The best structure is then used for fault detection. A neural network observer is developed and error stability is assessed. Two fault scenarios are considered.
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paper_title: Model-based diagnosis of an automotive engine using several types of fault models

paper_content:

Automotive engines is an important application for model-based diagnosis because of legislative regulations. A diagnosis system for the air-intake system of a turbo-charged engine is constructed. The design is made in a systematic way and follows a framework of hypothesis testing. Different types of sensor faults and leakages are considered. It is shown how many different types of fault models, e.g., additive and multiplicative faults, can be used within one common diagnosis system, and using the same underlying design principle. The diagnosis system is experimentally validated on a real engine using industry-standard dynamic test-cycles.
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paper_title: Embedded model-based fault diagnosis for on-board diagnosis of engine control systems

paper_content:

In this paper, a model-based fault diagnosis scheme for on-board diagnosis in spark ignition (SI) engine control systems is presented. The developed fault diagnosis system fully makes use of the available control structure and is embedded into the control loops. As a result, the implementation of the diagnosis system is realized with low demands on engineering costs, computational power and memory. The developed diagnosis scheme has been successfully applied to the air intake system of an SI-engine
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paper_title: Model-based fault detection and diagnosis with special input excitation applied to a modern diesel engine

paper_content:

Abstract Due to the rising complexity of many technical processes modern diagnosis systems have to supervise a multitude of hydraulic, mechanical, electromechanical and mechatronic components. Therefore model-based methods of fault-detection and diagnosis have been developed. These methods use mathematical process models to relate data of several measurable variables. Thus the diagnosis quality depends on the available sensor data. In order to obtain additional information with the given sensor configuration special input excitation signals can be used. This paper will describe a method to locate faults in multivariable systems using such input excitation and its application to the intake air system of a modern common rail Diesel engine. The presented method uses the knowledge of fault effects on the measured output, when the inputs are successively excited quasi-stationary, to determine the location of the fault. It has been applied successfully to differentiate air mass sensor faults from other process faults.
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paper_title: Robust strategy for intake leakage detection in diesel engines

paper_content:

Fault detection is motivated by the needs of guaranteeing high-performance engine behavior and regarding to the environmentally-based legislative regulations. An adaptive model-based observer strategy is applied for the fault detection and estimation. The hole estimation relies on the model accuracy and sensors precision. In this paper is provided by a model-based upper bound for leakage error estimation for threshold design by the mean of the observer sensitivity study. The proposed approach generates a threshold based only on the available measures even if faulty. Simulation results are provided using advanced Diesel engine developed under AMEsim.
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paper_title: Improved SI engine modelling techniques with application to fault detection

paper_content:

This paper applies a fault detection strategy to a Jaguar car engine. Real data, over several engine speeds, is used to obtain an improved model of a subsystem comprising of the air intake, the manifold dynamics and the engine pumping. Modelling of an air leak is also considered. Three practical fault scenarios are assessed, deemed important by the manufacturer. A nonlinear observer method is used to detect practical sensor faults and an air leak in the manifold. Isolation logic is designed with respect to a restricted set of sensor measurements. The effectiveness of both modelling and fault detection axe assessed. A practical aim is to limit engine pollutants caused by system faults.
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paper_title: Model Based Diagnosis for the Air Intake System of the SI-Engine

paper_content:

Because of legislative regulations like OBDII, on-board diagnosis has gained much interest lately. A model based approach is suggested for the diagnosis of the air intake system of an SI-engine. Im ...
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paper_title: Model Based Diagnosis of Leaks in the Air-Intake System of an SI-Engine

paper_content:

One important area of SI-engine diagnosis is the diagnosis of leakage in the air-intake system. This is because a leakage can cause increased emissions and drivability problems. A method for accura ...
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paper_title: Application of a data-driven monitoring technique to diagnose air leaks in an automotive diesel engine: A case study

paper_content:

This paper presents a case study of the application of a data-driven monitoring technique to diagnose air leaks in an automotive diesel engine. Using measurement signals taken from the sensors/actuators which are present in a modern automotive vehicle, a data-driven diagnostic model is built for condition monitoring purposes. Detailed investigations have shown that measured signals taken from the experimental test-bed often contain redundant information and noise due to the nature of the process. In order to deliver a clear interpretation of these measured signals, they therefore need to undergo a 'compression' and an 'extraction' stage in the modelling process. It is at this stage that the proposed data-driven monitoring technique plays a significant role by taking only the important information of the original measured signals for fault diagnosis purposes. The status of the engine's performance is then monitored using this diagnostic model. This condition monitoring process involves two separate stages of fault detection and root-cause diagnosis. ::: ::: The effectiveness of this diagnostic model was validated using an experimental automotive 1.9L four-cylinder diesel engine embedded in a chassis dynamometer in an engine test-bed. Two joint diagnostics plots were used to provide an accurate and sensitive fault detection process. Using the proposed model, small air leaks in the inlet manifold plenum chamber with a diameter size of 2-6 turn were accurately detected. Further analyses using contribution to T-2 and Q statistics show the effect of these air leaks on fuel consumption. It was later discovered that these air leaks may contribute to emissions fault. ::: ::: In comparison to the existing model-based approaches, the proposed method has several benefits: (i) it makes no simplifying assumptions, as the model is built entirely from the measured signals; (ii) it is simple and straight-forward; (iii) there is no additional hardware required for modelling; (iv) it is a time and cost-efficient way to deliver condition monitoring (i.e. fault diagnosis application); (v) it is capable of pin-pointing the root-cause and the effect of the problem; and (vi) it is feasible to be implemented in practice. (c) 2005 Elsevier Ltd. All rights reserved.
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paper_title: Using hypothesis testing theory to evaluate principles for leakage diagnosis of automotive engines

paper_content:

Two different methods for diagnosing leakages in the air path of an automotive engine are investigated. The first is based on a comparison between measured and estimated air flows. The second is based on an estimation of the leakage area. The two methods are compared by using a framework of hypothesis testing and especially the power function. The investigation is made first in theory and then also on a real engine. The conclusion is that the principle based on the estimated leakage area, gives a better power function and is therefore the best choice if only leakage detection is considered. However, if also other faults need to be diagnosed, it is shown that the sensitivity to these other faults may be better with the principle based on comparison of estimated and measured air flow. © 2003 Elsevier Ltd. All rights reserved.
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paper_title: Model-based adaptive observers for intake leakage detection in diesel engines

paper_content:

This paper studies the problem of diesel engine diagnosis by means of model-based adaptive observers. The problem is motivated by the needs of guarantee high-performance engine behavior and in particular to respect the environmentally-based legislative regulations. The complexity of the intake systems of this type of engine makes this task particularly arduous and requires to constantly monitor and diagnose the engine operation. The development and application of two different nonlinear adaptive observers for intake leakage estimation is the goal of this work. The proposed model-based adaptive observers approach allows estimating a variable that is directly related to the presence of leakage, e.g., hole radius. Monitoring and diagnostic tasks, with this kind of approach, are straightforward. Two different approaches, whose main difference is on observer adaptation law structure are studied. One approach is based on fixed gains while the other method has variable gain. The paper also includes experimental results of the two studied methods on a four cylinder Diesel engine testbed.
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paper_title: Model-based diagnosis of an automotive engine using several types of fault models

paper_content:

Automotive engines is an important application for model-based diagnosis because of legislative regulations. A diagnosis system for the air-intake system of a turbo-charged engine is constructed. The design is made in a systematic way and follows a framework of hypothesis testing. Different types of sensor faults and leakages are considered. It is shown how many different types of fault models, e.g., additive and multiplicative faults, can be used within one common diagnosis system, and using the same underlying design principle. The diagnosis system is experimentally validated on a real engine using industry-standard dynamic test-cycles.
---
paper_title: Diagnosis and Prognosis of Automotive Systems: motivations, history and some results

paper_content:

Abstract The field of automotive engineering has seen an explosion in the presence of on-board electronic components and systems vehicles since the 1970s. This growth was initially motivated by the introduction of emissions regulations that led to the widespread application of electronic engine controls. A secondary but important consequence of these developments was the adoption of on-board diagnostics regulations aimed at insuring that emission control systems would operate as intended for a prescribed period of time (or vehicle mileage). In addition, the presence of micro-controllers on-board the vehicle has led to a proliferation of other functions related to safety and customer convenience, and implemented through electronic systems and related software, thus creating the need for more sophisticated on-board diagnostics. Today, a significant percentage of the software code in an automobile is devoted to diagnostic functions. This paper presents an overview of diagnostic needs and requirements in the automotive industry, illustrates some of the challenges that are associated with satisfying these requirements, and proposes some future directions.
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paper_title: Optical Diagnostics of Late-Injection Low-Temperature Combustion in a Heavy-Duty Diesel Engine

paper_content:

A late injection, high exhaust-gas recirculation (EGR)-rate, low-temperature combustion strategy was investigated in a heavy-duty diesel engine using a suite of optical diagnostics: chemiluminescence for visualization of ignition and combustion, laser Mie scattering for liquid fuel imaging, planar laser-induced fluorescence (PLIF) for both OH and vapor-fuel imaging, and laser-induced incandescence (LII) for soot imaging. Fuel is injected at top dead center when the in-cylinder gases are hot and dense. Consequently, the maximum liquid fuel penetration is 27 mm, which is short enough to avoid wall impingement. The cool flame starts 4.5 crank angle degrees (CAD) after the start of injection (ASI), midway between the injector and bowl-rim, and likely helps fuel to vaporize. Within a few CAD, the cool-flame combustion reaches the bowl-rim. A large premixed combustion occurs near 9 CAD ASI, close to the bowl rim. Soot is visible shortly afterwards along the walls, typically between two adjacent jets at the head vortex location. OH PLIF indicates that premixed combustion first occurs within the jet and then spreads along the bowl rim in a thin layer, surrounding soot pockets at the start of the mixing-controlled combustion phase near 17 CAD ASI. During the mixing-controlled phase, soot is not fully oxidized and is still present near the bowl-rim late in the cycle. At the end of combustion near 27 CAD ASI, averaged PLIF images indicate two separate zones. OH PLIF appears near the bowl rim, while broadband PLIF persists late in the cycle near the injector. The most likely source of broadband PLIF is unburned fuel, which indicates that the near-injector region is a potential source of unburned hydrocarbons.
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paper_title: On Board Vehicle Diagnostics

paper_content:

Vehicle on-board diagnostics has been evolving rapidly over the last 20 years. What began as a small set manufacturer-specific tests and communication protocols has evolved into a complex, comprehensive diagnostic system able to detect literally hundreds of failures that could cause drivability concerns or emission increases. This rapid evolution has been driven, in part, by California's OBD-II regulations as well as need for manufacturers to provide comprehensive diagnostics to allow technicians to service the complex engine and transmission controls on today's vehicles. As the technology improves, states are relying on on-board diagnostics for Inspection/Maintenance programs in place of more costly tailpipe emission tests. The recent introduction of a standardized CAN communication link provides fast and easy access to internal control module data and opens the way for new wireless telematics technologies like prognostics and remote diagnostics. This paper explores the history and future opportunities brought about by this OBD technology.
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paper_title: An Integrated Diagnostic Development Process for Automotive Engine Control Systems

paper_content:

Theory and applications of model-based fault diagnosis have progressed significantly in the last four decades. In addition, there has been increased use of model-based design and testing in the automotive industry to reduce design errors, perform rapid prototyping, and hardware-in-the-loop simulation (HILS). This paper presents a new model-based diagnostic development process for automotive engine control systems. This process seamlessly employs a graph-based dependency model and mathematical models for online/offline diagnosis. The hybrid method improves the diagnostic system's accuracy and consistency, utilizes existing validated knowledge on empirical models, enables remote diagnosis, and responds to the challenges of increased system complexity. The development platform consists of an engine electronic control unit (ECU) rapid prototyping system and HILS equipment - the air intake subsystem (AIS). The diagnostic strategy is tested and validated using the HILS platform.
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paper_title: Application of Signal Analysis and Data-driven Approaches to Fault Detection and Diagnosis in Automotive Engines

paper_content:

The modern era of sophisticated automobiles is necessitating the development of generic and automated embedded fault diagnosis tools. Future vehicles are expected to contain more than one hundred complex electronic control units (ECUs) and data acquisition systems to control and monitor large number of system variables in real-time. There exists an abundant amount of literature on fault detection and diagnosis (FDD). However, these techniques are developed in isolation. In order to solve the problem of FDD in complex systems, such as modern vehicles, a hybrid methodology combining different techniques is needed. Here, we apply an approach based on signal analysis that combines various signal processing and statistical learning techniques for real-time FDD in automotive engines. The data under several scenarios is collected from an engine model running in a real-time simulator and controlled by an ECU.
---
paper_title: Application of an Effective Data-Driven Approach to Real-time time Fault Diagnosis in Automotive Engines

paper_content:

A dominant thrust in modern automotive industry is the development of "smart service systems" for the comfort of customers. The current on-board diagnosis systems embedded in the automobiles follow conventional rule-based diagnosis procedures, and may benefit from the introduction of sophisticated artificial intelligence and pattern recognition-based procedures in terms of diagnostic accuracy. Here, we present a mode-invariant fault diagnosis procedure that is based on data -driven approach, and show its applicability to automotive engines. The proposed approach achieves high-diagnostic accuracy by detecting the faults as soon as they occur. It uses statistical hypothesis tests to detect faults, a wavelet-based preprocessing of the data, and pattern recognition techniques for classifying various faults in engines. We simulate the Toyota Camry 544N Engine SIMULINK model in a real-time simulator and controlled by a prototype ECU (Electronic Control Unit). The engine model is simulated under several operating conditions (pedal angle, engine speed, etc), and pre-and post-fault data is collected for eight engine faults with different severity levels, and a database of cases is created for applying the presented approach. The results demonstrate that appealing diagnostic accuracy and fault severity estimation are possible with pattern recognition-based techniques, and, in particular, with the support vector machines.
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paper_title: Integration of fdi functional units into embedded tracking control loops and its application to FDI in engine control systems

paper_content:

A scheme is presented for the integration of FDI functional units into control loops embedded in mechatronic systems. The core of this FDI scheme is to design the FDI functional units by making use of the available tracking control structure. The developed FDI scheme is applied to the air-intake control system of an SI-engine
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paper_title: Developing a fault tolerant power‐train control system by integrating design of control and diagnostics

paper_content:

Fault detection, isolation and fault tolerant control are investigated for an spark ignition engine. Fault tolerant control refers to a strategy in which the desired stability and robustness of the control system are guaranteed in the presence of faults. In an attempt to realize fault tolerant control, a methodology for integrated design of control and fault diagnostics is proposed. Specifically, the integrated design of control and diagnostics is achieved by combining the integral sliding mode control methodology and observers with hypothesis testing. Information obtained from integral sliding mode control and from observers with hypothesis testing is utilized so that a fault can be detected, isolated and compensated. As an application example, the air and fuel dynamics of an IC engine are considered. A mean value engine model is developed and implemented in Simulink®. The air and fuel dynamics of the engine are identified using experimental data. The proposed algorithm for integration of control and diagnostics is then validated using the identified engine model. Copyright © 2001 John Wiley & Sons, Ltd.
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paper_title: Automotive engine diagnosis and control via nonlinear estimation

paper_content:

The aim of this article is to explore a possible approach to the problem of designing control and diagnostic strategies for future generations of automotive engines. The work described focuses on the use of physical models to estimate unmeasured or unmeasurable variables and parameters, to be used for control and diagnostic purposes. We introduce the mean value engine model used, present a conceptual strategy for combined control and diagnosis focusing mainly on the problem of air fuel ratio control, review basic concepts related to estimation in nonlinear systems, and propose various forms of the estimators that serve different objectives. We illustrate estimation problems in the context of a simplified engine model, assuming both linear and nonlinear measurement of oxygen concentration in the exhaust. Some simulation and experimental results related to estimation-based control and diagnosis are shown.
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paper_title: Nonlinear parity equation based residual generation for diagnosis of automotive engine faults

paper_content:

Abstract The parity equation residual generation method is a model-based fault detection and isolation scheme that has been applied with some success to the problem of monitoring the health of engineering systems. However, this scheme fails when applied to significantly nonlinear systems. This paper presents the application of a nonlinear parity equation residual generation scheme that uses forward and inverse dynamic models of nonlinear systems, to the problem of diagnosing sensor and actuator faults in an internal combustion engine, during execution of the United States Environmental Protection Agency Inspection and Maintenance 240 driving cycle. The Nonlinear AutoRegressive Moving Average Model with eXogenous inputs technique is used to identify the engine models required for residual generation. The proposed diagnostic scheme is validated experimentally and is shown to be sensitive to a number of input and sensor faults while remaining robust to the unmeasured load torque disturbance.
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paper_title: Embedded model-based fault diagnosis for on-board diagnosis of engine control systems

paper_content:

In this paper, a model-based fault diagnosis scheme for on-board diagnosis in spark ignition (SI) engine control systems is presented. The developed fault diagnosis system fully makes use of the available control structure and is embedded into the control loops. As a result, the implementation of the diagnosis system is realized with low demands on engineering costs, computational power and memory. The developed diagnosis scheme has been successfully applied to the air intake system of an SI-engine
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Title: A Survey on Diagnostic Methods for Automotive Engines

Section 1: INTRODUCTION
Description 1: An introductory section that covers the background, significance, and regulatory requirements of automotive onboard diagnostic systems.

Section 2: APPROACHES TO FAULT DETECTION AND DIAGNOSIS OF ENGINEERED SYSTEMS
Description 2: A comprehensive review of various methods available for diagnostics of engineered systems, categorized under data-driven and model-based methods.

Section 3: MONITORING REQUIREMENTS FOR AUTOMOTIVE ENGINES AND RELEVANT WORK
Description 3: Detailed description of major monitoring requirements for modern automotive engines, including their purpose, malfunction criteria, and recent work.

Section 4: DIAGNOSIS OF SENSOR FAULTS AND LEAKS IN AUTOMOTIVE ENGINES
Description 4: Discussion of sensor faults and leaks in automotive engines, including detection and diagnosis methods using both data-driven and model-based approaches.

Section 5: EMERGING AREAS OF RESEARCH IN FAULT DIAGNOSIS OF POWER TRAIN SYSTEMS
Description 5: Exploration of recent research efforts and emerging topics in diagnostic methods for advanced combustion, remote diagnostics, and integration of fault detection with engine control design.

Section 6: FUNDING
Description 6: Statement regarding the funding of the research, if any.