Applying CBM and PHM concepts with reliability approach for Blowout Preventer (BOP): a literature review
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paper_title: Improved methods for reliability assessments of safety-critical systems: An application example for BOP systems

paper_content:

The failure of the Deepwater Horizon drilling rig's blowout preventer has been pointed to as one of the main causes of the Macondo accident on April 10th 2010. The blowout preventer system is one the most important safety barriers in a hydrocarbon well. The accident has created a demand for improved methods of assessing the reliability of blowout preventer systems. The objective of this master thesis is to propose improvements to current reliability assessment methods for complex safety critical systems such as the blowout preventer. The report begins by describing the blowout preventer system. It is a system consisting of two main subsea parts containing the annular and ram blowout preventer valves which are used to seal off a well in the event of a subsea well kick. These annular and ram type preventers are governed by an electro-hydraulic control system which is operated by human interaction from control panels located on the rig floor. A functional analysis of the blowout preventer system is presented next. Essential functions are defined, and performance criteria for these functions identified. An approach to classification of blowout preventer functions is also presented, before the report moves on to the analysis of four main operational situations to which the blowout preventer is exposed, and whose characteristics have implications for the system's ability to act as a safety barrier. The pros and cons of different widely used blowout preventer system configurations is also discussed. Three main types of configurations are mentioned in the report; the \emph{modern} configuration, \emph{traditional} configuration and the Deepwater Horizon blowout preventer system configuration. A literature survey which documents previous blowout preventer reliability studies performed by Per Holand on behalf of SINTEF is presented. An evaluation of validity of the operational assumptions which have been made in these previous studies is also provided, such as such as assumptions regarding operational situations, failure input data, and several important assumptions regarding testing of blowout preventer systems. Regulations and guidelines which are relevant to blowout preventer reliability are also described here. The report further discusses how the blowout preventer may fail, and which types of failures modes are considered critical from a safety perspective. Some theoretic principles behind common cause failures are presented, along with a description of how common cause failures should be included in reliability assessments of safety critical systems through an approach called the \emph{PDS approach}. This is followed by a discussion of possible sources for common cause failures in the blowout preventer system. As a suggestion towards how reliability assessments of blowout preventers can be improved, and some of the identified challenges solved, a reliability quantification method is presented. The method is based on post-processing of minimal cut sets from a fault tree analysis of the blowout preventer system, and produces more conservative and accurate approximations of the reliability than those produced through conventonal methods. The method is also capable of taking into account common cause failures. The results from the calculations are presented and discussed. An event tree which illustrates the effect from an escalated well control situation on the blowout preventer's ability to act as a safety barrier is also presented, along with a discussion of how blowout preventer reliability could possibly be more appropriately assessed through event tree analysis. Finally, the conclusions from the thesis are provided. The main conclusions are that the approach based on fault trees and post-processing of minimal cut sets can certainly be used to improve the quality of blowout preventer reliability estimates, and also provides a sound platform for including common cause failures in the analysis. Another key finding is that the fault tree, which is a "static model", poorly illustrates the criticality of "preventer-specific" components in escalated well control situations, since the unavailablility of certain functions due to operational conditions has little or no implication of the reliability estimates produced. In contrast, the criticality of common control system components is certainly emphasised by the fault tree model. The author suggests that quantificaiton of blowout preventer systems through fault tree analysis should be supplemented by event tree analysis to better evaluate the effect from escalation of the well control situation. Furthermore, the author recommends that a test coverage factor should be included when calculating the safety unavailability of components exclusive to shearing rams, since these cannot be fully function tested through conventional, non-destructive blowout preventer tests. It is also recommended that the industry investigate the accuracy with which the location of tool joints in the wellbore annulus can be determined through current methods. Improper spacing of tool joints is critical in a well control situation where the shear rams must be activated.
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paper_title: Strategies for Diagnosis

paper_content:

Diagnosis is among the dominant applications of expert systems technology today. In the past, most diagnostic systems were built using some form of rule-based production system. Recently, however, many new strategies have emerged to support much more complex reasoning for diagnosis. A survey of the architectures for the complex strategies now available for diagnosis is presented.
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paper_title: Sensor Systems for Prognostics and Health Management

paper_content:

Prognostics and health management (PHM) is an enabling discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk. Sensor systems are needed for PHM to monitor environmental, operational, and performance-related characteristics. The gathered data can be analyzed to assess product health and predict remaining life. In this paper, the considerations for sensor system selection for PHM applications, including the parameters to be measured, the performance needs, the electrical and physical attributes, reliability, and cost of the sensor system, are discussed. The state-of-the-art sensor systems for PHM and the emerging trends in technologies of sensor systems for PHM are presented.
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paper_title: Improved methods for reliability assessments of safety-critical systems: An application example for BOP systems

paper_content:

The failure of the Deepwater Horizon drilling rig's blowout preventer has been pointed to as one of the main causes of the Macondo accident on April 10th 2010. The blowout preventer system is one the most important safety barriers in a hydrocarbon well. The accident has created a demand for improved methods of assessing the reliability of blowout preventer systems. The objective of this master thesis is to propose improvements to current reliability assessment methods for complex safety critical systems such as the blowout preventer. The report begins by describing the blowout preventer system. It is a system consisting of two main subsea parts containing the annular and ram blowout preventer valves which are used to seal off a well in the event of a subsea well kick. These annular and ram type preventers are governed by an electro-hydraulic control system which is operated by human interaction from control panels located on the rig floor. A functional analysis of the blowout preventer system is presented next. Essential functions are defined, and performance criteria for these functions identified. An approach to classification of blowout preventer functions is also presented, before the report moves on to the analysis of four main operational situations to which the blowout preventer is exposed, and whose characteristics have implications for the system's ability to act as a safety barrier. The pros and cons of different widely used blowout preventer system configurations is also discussed. Three main types of configurations are mentioned in the report; the \emph{modern} configuration, \emph{traditional} configuration and the Deepwater Horizon blowout preventer system configuration. A literature survey which documents previous blowout preventer reliability studies performed by Per Holand on behalf of SINTEF is presented. An evaluation of validity of the operational assumptions which have been made in these previous studies is also provided, such as such as assumptions regarding operational situations, failure input data, and several important assumptions regarding testing of blowout preventer systems. Regulations and guidelines which are relevant to blowout preventer reliability are also described here. The report further discusses how the blowout preventer may fail, and which types of failures modes are considered critical from a safety perspective. Some theoretic principles behind common cause failures are presented, along with a description of how common cause failures should be included in reliability assessments of safety critical systems through an approach called the \emph{PDS approach}. This is followed by a discussion of possible sources for common cause failures in the blowout preventer system. As a suggestion towards how reliability assessments of blowout preventers can be improved, and some of the identified challenges solved, a reliability quantification method is presented. The method is based on post-processing of minimal cut sets from a fault tree analysis of the blowout preventer system, and produces more conservative and accurate approximations of the reliability than those produced through conventonal methods. The method is also capable of taking into account common cause failures. The results from the calculations are presented and discussed. An event tree which illustrates the effect from an escalated well control situation on the blowout preventer's ability to act as a safety barrier is also presented, along with a discussion of how blowout preventer reliability could possibly be more appropriately assessed through event tree analysis. Finally, the conclusions from the thesis are provided. The main conclusions are that the approach based on fault trees and post-processing of minimal cut sets can certainly be used to improve the quality of blowout preventer reliability estimates, and also provides a sound platform for including common cause failures in the analysis. Another key finding is that the fault tree, which is a "static model", poorly illustrates the criticality of "preventer-specific" components in escalated well control situations, since the unavailablility of certain functions due to operational conditions has little or no implication of the reliability estimates produced. In contrast, the criticality of common control system components is certainly emphasised by the fault tree model. The author suggests that quantificaiton of blowout preventer systems through fault tree analysis should be supplemented by event tree analysis to better evaluate the effect from escalation of the well control situation. Furthermore, the author recommends that a test coverage factor should be included when calculating the safety unavailability of components exclusive to shearing rams, since these cannot be fully function tested through conventional, non-destructive blowout preventer tests. It is also recommended that the industry investigate the accuracy with which the location of tool joints in the wellbore annulus can be determined through current methods. Improper spacing of tool joints is critical in a well control situation where the shear rams must be activated.
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paper_title: The EDF failure reporting system process, presentation and prospects

paper_content:

Abstract This paper describes the procedure Electricite de France uses to exploit the information on pressurized water reactor operation it receives back from the field (operation feedback). The first requirement in analyzing such data is a knowledge of past records. The first step, therefore, is to record the data, particularly events occurring on the plant and failures occurring on equipment, in large reliability data banks. However, the ‘raw’ information stored is rarely usable directly. The first step in the second stage—analysis—is to review and qualify the data before using it for any purpose. This difficult, but essential, review provides valuable information on the improvement of equipment reliability. The greater knowledge of plant and equipment behaviour, and the damage mechanisms involved, allows: • ⊎ safety to be kept at a high level: operation feedback is also essential for probabilistic safety studies, • ⊎ improvement of availability and preventive maintenance practices, • ⊎ correction of the initial design (design changes) and help for designing future plants. Finally, operation feedback is a source of progress. Although it requires heavy initial investment, it is also a source of profit. It is a source of learning. The analysis results make it possible to define more suitable procedures and better preventive maintenance practices and thus improve the operation and safety of existing and future plants.
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paper_title: Reliability centered maintenance

paper_content:

Abstract Reliability centered maintenance (RCM) is a method for maintenance planning developed within the aircraft industry and later adapted to several other industries and military branches. This paper presents a structured approach to RCM, and discusses the various steps in the approach. The RCM method provides a framework for utilizing operating experience in a more systematic way. The requirements for reliability models and data are therefore highlighted. The gap between maintenance practitioners and scientists working with maintenance optimization models is discussed, together with some future challenges for RCM.
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paper_title: Sensor Systems for Prognostics and Health Management

paper_content:

Prognostics and health management (PHM) is an enabling discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk. Sensor systems are needed for PHM to monitor environmental, operational, and performance-related characteristics. The gathered data can be analyzed to assess product health and predict remaining life. In this paper, the considerations for sensor system selection for PHM applications, including the parameters to be measured, the performance needs, the electrical and physical attributes, reliability, and cost of the sensor system, are discussed. The state-of-the-art sensor systems for PHM and the emerging trends in technologies of sensor systems for PHM are presented.
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paper_title: MULTIPLE FAILURE MODES ANALYSIS AND WEIGHTED RISK PRIORITY NUMBER EVALUATION IN FMEA

paper_content:

Traditionally, failure mode and effects analysis (FMEA) only considers the impact of single failure on the system. For large and complex systems, since multiple failures of components exist, assessing multiple failure modes with all possible combinations is impractical. Pickard et al. [1] introduced a useful method to simultaneously analyze multiple failures for complex systems. However, they did not indicate which failures need to be considered and how to combine them appropriately. This paper extends Pickard’s work by proposing a minimum cut set based method for assessing the impact of multiple failure modes. In addition, traditional FMEA is made by addressing problems in an order from the biggest risk priority number (RPN) to the smallest ones. However, one disadvantage of this approach is that it ignores the fact that three factors (Severity (S), Occurrence (O), Detection (D)) (S, O, D) have the different weights in system rather than equality. For examples, reasonable weights for factors S, O are higher than the weight of D for some non-repairable systems. In this paper, we extended the definition of RPN by multiplying it with a weight parameter, which characterize the importance of the failure causes within the system. Finally, the effectiveness of the method is demonstrated with numerical examples.
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paper_title: Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis

paper_content:

Abstract This paper describes a new technique, based on fuzzy logic, for prioritizing failures for corrective actions in a Failure Mode, Effects and Criticality Analysis (FMECA). As in a traditional criticality analysis, the assessment is based on the severity, frequency of occurrence, and detectability of an item failure. However, these parameters are here represented as members of a fuzzy set, combined by matching them against rules in a rule base, evaluated with min-max inferencing, and then defuzzified to assess the riskiness of the failure. This approach resolves some of the problems in traditional methods of evaluation and it has several advantages compared to strictly numerical methods: 1) it allows the analyst to evaluate the risk associated with item failure modes directly using the linguistic terms that are employed in making the criticality assessment; 2) ambiguous, qualitative, or imprecise information, as well as quantitative data, can be used in the assessment and they are handled in a consistent manner; and 3) it gives a more flexible structure for combining the severity, occurrence, and detectability parameters. Two fuzzy logic based approaches for assessing criticality are presented. The first is based on the numerical rankings used in a conventional Risk Priority Number (RPN) calculation and uses crisp inputs gathered from the user or extracted from a reliability analysis. The second, which can be used early in the design process when less detailed information is available, allows fuzzy inputs and also illustrates the direct use of the linguistic rankings defined for the RPN calculations.
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paper_title: Risk analysis of analytical validations by probabilistic modification of FMEA

paper_content:

Abstract Risk analysis is a valuable addition to validation of an analytical chemistry process, enabling not only detecting technical risks, but also risks related to human failures. Failure Mode and Effect Analysis (FMEA) can be applied, using a categorical risk scoring of the occurrence, detection and severity of failure modes, and calculating the Risk Priority Number (RPN) to select failure modes for correction. We propose a probabilistic modification of FMEA, replacing the categorical scoring of occurrence and detection by their estimated relative frequency and maintaining the categorical scoring of severity. In an example, the results of traditional FMEA of a Near Infrared (NIR) analytical procedure used for the screening of suspected counterfeited tablets are re-interpretated by this probabilistic modification of FMEA. Using this probabilistic modification of FMEA, the frequency of occurrence of undetected failure mode(s) can be estimated quantitatively, for each individual failure mode, for a set of failure modes, and the full analytical procedure.
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paper_title: A New Approach for Prioritization of Failure Modes in Design FMEA using ANOVA

paper_content:

The traditional Failure Mode and Effects Analysis (FMEA) uses Risk Priority Number (RPN) to evaluate the risk level of a component or process. The RPN index is determined by calculating the product of severity, occurrence and detection indexes. The most critically debated disadvantage of this approach is that various sets of these three indexes may produce an identical value of RPN. This research paper seeks to address the drawbacks in traditional FMEA and to propose a new approach to overcome these shortcomings. The Risk Priority Code (RPC) is used to prioritize failure modes, when two or more failure modes have the same RPN. A new method is proposed to prioritize failure modes, when there is a disagreement in ranking scale for severity, occurrence and detection. An Analysis of Variance (ANOVA) is used to compare means of RPN values. SPSS (Statistical Package for the Social Sciences) statistical analysis package is used to analyze the data. The results presented are based on two case studies. It is found that the proposed new methodology/approach resolves the limitations of traditional FMEA approach. unacceptable failure effects occur, design changes are made to mitigate those effects. The criticality part of the analysis prioritizes the failures for corrective action based on the probability of the item's failure mode and the severity of its effects. It uses linguistic terms to rank the probability of the failure mode occurrence, the severity of its failure effect and the probability of the failure being detected on a numeric scale from 1 to 10. These rankings are then multiplied to give the Risk Priority Number. Failure modes having a high RPN are assumed to be more important and given a higher priority than those having a lower RPN (2). II. RPN METHODOLOGY
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paper_title: System failure behavior and maintenance decision making using, RCA, FMEA and FM

paper_content:

Purpose – The purpose of this paper is to permit the system reliability analysts/managers/engineers to model, analyze and predict the behavior of industrial systems in a more realistic and consistent manner and plan suitable maintenance strategies accordingly.Design/methodology/approach – Root cause analysis (RCA), failure mode effect analysis (FMEA) and fuzzy methodology (FM) have been used by the authors to build an integrated framework, to facilitate the reliability/system analysts in maintenance planning. The factors contributing to system unreliability were analyzed using RCA and FMEA. The uncertainty related to performance of system is modeled using fuzzy synthesis of information.Findings – The in‐depth analysis of system is carried out using RCA and FMEA. The discrepancies associated with the traditional procedure of risk ranking in FMEA are modeled using decision making system based on fuzzy methodology. Further, to cope up with imprecise, uncertain and subjective information related to system per...
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paper_title: Utility Priority Number Evaluation for FMEA

paper_content:

Traditionally, decisions on how to improve an operation are based on risk priority number (RPN) in the failure mode and effects analysis (FMEA). Many scholars questioned the RPN method and proposed some new methods to improve the decision process, but these methods are only measuring from the risks viewpoint while ignoring the importance of corrective actions. The corrective actions may be interdependent; hence, if the implementation of corrective actions is in proper order, selection may maximize the improvement effect, bring favorable results in the shortest times, and provide the lowest cost. This study aims to evaluate the structure of hierarchy and interdependence of corrective action by interpretive structural model (ISM), then to calculate the weight of a corrective action through the analytic network process (ANP), then to combine the utility of corrective actions and make a decision on improvement priority order of FMEA by utility priority number (UPN). Finally, it verifies the feasibility and effectiveness of this method by application to a case study.
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paper_title: Intelligent Predictive Decision Support System for Condition-Based Maintenance

paper_content:

The high costs in maintaining today’s complex and sophisticated equipment make it necessary to enhance modern maintenance management systems. Conventional condition-based maintenance (CBM) reduces the uncertainty of maintenance according to the needs indicated by the equipment condition. The intelligent predictive decision support system (IPDSS) for condition-based maintenance (CBM) supplements the conventional CBM approach by adding the capability of intelligent condition-based fault diagnosis and the power of predicting the trend of equipment deterioration. An IPDSS model, based on the recurrent neural network (RNN) approach, was developed and tested and run for the critical equipment of a power plant. The results showed that the IPDSS model provided reliable fault diagnosis and strong predictive power for the trend of equipment deterioration. These valuable results could be used as input to an integrated maintenance management system to pre-plan and pre-schedule maintenance work, to reduce inventory costs for spare parts, to cut down unplanned forced outage and to minimise the risk of catastrophic failure.
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paper_title: Modelling using UML and BPMN the integration of open reliability, maintenance and condition monitoring management systems: An application in an electric transformer system

paper_content:

Maintenance management of an industrial plant has been always a complex activity. Nowadays Computerized Maintenance Management Systems (CMMSs) help to organize information and thus to carry out maintenance activities in a more efficient way. The emergence of new ICT has increased also the use of Condition Based Maintenance (CBM) systems and the application of Reliability Centred Maintenance (RCM) analysis. Each system is proved to provide benefits to the maintenance management. However when all the systems are adopted, the lack of integration among them can prevent the maximum exploitation of their capabilities. This work aims at fulfilling this gap, proposing an e-maintenance integration platform that combines the features of the three main systems. The methodology and the reference open standards used to develop the platform are exposed. UML-BPMN diagrams represent the emerging algorithms of the designed system. The final product, a software demo is implemented in an electric transformer.
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paper_title: Common cause failures in safety instrumented systems on oil and gas installations: Implementing defense measures through function testing

paper_content:

This paper presents a common cause failure (CCF) defense approach for safety instrumented systems (SIS) in the oil and gas industry. The SIS normally operates in the low demand mode, which means that regular testing and inspection are required to reveal SIS failures. The CCF defense approach comprises checklists and analytical tools which may be integrated with current approaches for function testing, inspection and follow-up. The paper focuses on how defense measures may be implemented to increase awareness of CCFs, to improve the ability to detect CCFs, and to avoid introducing new CCFs. The CCF defense approach may also be applicable for other industry sectors.
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paper_title: On condition based maintenance policy

paper_content:

Abstract In the case of a high-valuable asset, the Operation and Maintenance (O&M) phase requires heavy charges and more efforts than the installation (construction) phase, because it has long usage life and any accident of an asset during this period causes catastrophic damage to an industry. Recently, with the advent of emerging Information Communication Technologies (ICTs), we can get the visibility of asset status information during its usage period. It gives us new challenging issues for improving the efficiency of asset operations. One issue is to implement the Condition-Based Maintenance (CBM) approach that makes a diagnosis of the asset status based on wire or wireless monitored data, predicts the assets abnormality, and executes suitable maintenance actions such as repair and replacement before serious problems happen. In this study, we have addressed several aspects of CBM approach: definition, related international standards, procedure, and techniques with the introduction of some relevant case studies that we have carried out.
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paper_title: Reliability centered maintenance

paper_content:

Abstract Reliability centered maintenance (RCM) is a method for maintenance planning developed within the aircraft industry and later adapted to several other industries and military branches. This paper presents a structured approach to RCM, and discusses the various steps in the approach. The RCM method provides a framework for utilizing operating experience in a more systematic way. The requirements for reliability models and data are therefore highlighted. The gap between maintenance practitioners and scientists working with maintenance optimization models is discussed, together with some future challenges for RCM.
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paper_title: Condition based maintenance systems : An Investigation of Technical Constituets and Organizational Aspects

paper_content:

Condition based maintenance systems : An Investigation of Technical Constituets and Organizational Aspects
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paper_title: Sensor Systems for Prognostics and Health Management

paper_content:

Prognostics and health management (PHM) is an enabling discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk. Sensor systems are needed for PHM to monitor environmental, operational, and performance-related characteristics. The gathered data can be analyzed to assess product health and predict remaining life. In this paper, the considerations for sensor system selection for PHM applications, including the parameters to be measured, the performance needs, the electrical and physical attributes, reliability, and cost of the sensor system, are discussed. The state-of-the-art sensor systems for PHM and the emerging trends in technologies of sensor systems for PHM are presented.
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paper_title: Self-maintenance and engineering immune systems: Towards smarter machines and manufacturing systems

paper_content:

Abstract This paper discusses the state-of-the-art research in the areas of self-maintenance and engineering immune systems (EIS) for machines with smarter adaptability to operating regime changes in future manufacturing systems. Inspired by the biological immune and nervous systems, the authors are introducing the transformation of prognostics and health management (PHM) to engineering immune systems (EIS). First, an overview on PHM is introduced. Its transformation toward resilient systems, self-maintenance systems, and engineering immune systems is also discussed. Finally, new concepts in developing future biological-based smarter machines based on autonomic computing and cloud computing are discussed.
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paper_title: Intelligent Fault Diagnosis and Prognosis for Engineering Systems

paper_content:

PREFACE. ACKNOWLEDGMENTS. PROLOGUE. 1 INTRODUCTION. 1.1 Historical Perspective. 1.2 Diagnostic and Prognostic System Requirements. 1.3 Designing in Fault Diagnostic and Prognostic Systems. 1.4 Diagnostic and Prognostic Functional Layers. 1.5 Preface to Book Chapters. 1.6 References. 2 SYSTEMS APPROACH TO CBM/PHM. 2.1 Introduction. 2.2 Trade Studies. 2.3 Failure Modes and Effects Criticality Analysis (FMECA). 2.4 System CBM Test-Plan Design. 2.5 Performance Assessment. 2.6 CBM/PHM Impact on Maintenance and Operations: Case Studies. 2.7 CBM/PHM in Control and Contingency Management. 2.8 References. 3 SENSORS AND SENSING STRATEGIES. 3.1 Introduction. 3.2 Sensors. 3.3 Sensor Placement. 3.4 Wireless Sensor Networks. 3.5 Smart Sensors. 3.6 References. 4 SIGNAL PROCESSING AND DATABASE MANAGEMENT SYSTEMS. 4.1 Introduction. 4.2 Signal Processing in CBM/PHM. 4.3 Signal Preprocessing. 4.4 Signal Processing. 4.5 Vibration Monitoring and Data Analysis. 4.6 Real-Time Image Feature Extraction and Defect/Fault Classification. 4.7 The Virtual Sensor. 4.8 Fusion or Integration Technologies. 4.9 Usage-Pattern Tracking. 4.10 Database Management Methods. 4.11 References. 5 FAULT DIAGNOSIS. 5.1 Introduction. 5.2 The Diagnostic Framework. 5.3 Historical Data Diagnostic Methods. 5.4 Data-Driven Fault Classification and Decision Making. 5.5 Dynamic Systems Modeling. 5.6 Physical Model-Based Methods. 5.7 Model-Based Reasoning. 5.8 Case-Based Reasoning (CBR). 5.9 Other Methods for Fault Diagnosis. 5.10 A Diagnostic Framework for Electrical/Electronic Systems. 5.11 Case Study: Vibration-Based Fault Detection and Diagnosis for Engine Bearings. 5.12 References. 6 FAULT PROGNOSIS. 6.1 Introduction. 6.2 Model-Based Prognosis Techniques. 6.3 Probability-Based Prognosis Techniques. 6.4 Data-Driven Prediction Techniques. 6.5 Case Studies. 6.6 References. 7 FAULT DIAGNOSIS AND PROGNOSIS PERFORMANCE METRICS. 7.1 Introduction. 7.2 CBM/PHM Requirements Definition. 7.3 Feature-Evaluation Metrics. 7.4 Fault Diagnosis Performance Metrics. 7.5 Prognosis Performance Metrics. 7.6 Diagnosis and Prognosis Effectiveness Metrics. 7.7 Complexity/Cost-Benefit Analysis of CBM/PHM Systems. 7.8 References. 8 LOGISTICS: SUPPORT OF THE SYSTEM IN OPERATION. 8.1 Introduction. 8.2 Product-Support Architecture, Knowledge Base, and Methods for CBM. 8.3 Product Support without CBM. 8.4 Product Support with CBM. 8.5 Maintenance Scheduling Strategies. 8.6 A Simple Example. 8.7 References. APPENDIX. INDEX.
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paper_title: Condition based maintenance optimization for wind power generation systems under continuous monitoring

paper_content:

By utilizing condition monitoring information collected from wind turbine components, condition based maintenance (CBM) strategy can be used to reduce the operation and maintenance costs of wind power generation systems. The existing CBM methods for wind power generation systems deal with wind turbine components separately, that is, maintenance decisions are made on individual components, rather than the whole system. However, a wind farm generally consists of multiple wind turbines, and each wind turbine has multiple components including main bearing, gearbox, generator, etc. There are economic dependencies among wind turbines and their components. That is, once a maintenance team is sent to the wind farm, it may be more economical to take the opportunity to maintain multiple turbines, and when a turbine is stopped for maintenance, it may be more cost-effective to simultaneously replace multiple components which show relatively high risks. In this paper, we develop an optimal CBM solution to the above-mentioned issues. The proposed maintenance policy is defined by two failure probability threshold values at the wind turbine level. Based on the condition monitoring and prognostics information, the failure probability values at the component and the turbine levels can be calculated, and the optimal CBM decisions can be made accordingly. A simulation method is developed to evaluate the cost of the CBM policy. A numerical example is provided to illustrate the proposed CBM approach. A comparative study based on commonly used constant-interval maintenance policy demonstrates the advantage of the proposed CBM approach in reducing the maintenance cost.
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paper_title: A Wireless Sensor System for Prognostics and Health Management

paper_content:

This paper introduces a novel radio-frequency-based wireless sensor system and describes its prognostics and health management functions. The wireless sensor system includes a radio frequency identification sensor tag, a wireless reader, and diagnostic-prognostic software. The software uses the sequential probability ratio test with a cross-validation procedure to detect anomalies, assess degradation, and predict failures. The prognostic performance of the sensor system is demonstrated by a field application.
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paper_title: Sensor Systems for Prognostics and Health Management

paper_content:

Prognostics and health management (PHM) is an enabling discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk. Sensor systems are needed for PHM to monitor environmental, operational, and performance-related characteristics. The gathered data can be analyzed to assess product health and predict remaining life. In this paper, the considerations for sensor system selection for PHM applications, including the parameters to be measured, the performance needs, the electrical and physical attributes, reliability, and cost of the sensor system, are discussed. The state-of-the-art sensor systems for PHM and the emerging trends in technologies of sensor systems for PHM are presented.
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paper_title: Intelligent Predictive Decision Support System for Condition-Based Maintenance

paper_content:

The high costs in maintaining today’s complex and sophisticated equipment make it necessary to enhance modern maintenance management systems. Conventional condition-based maintenance (CBM) reduces the uncertainty of maintenance according to the needs indicated by the equipment condition. The intelligent predictive decision support system (IPDSS) for condition-based maintenance (CBM) supplements the conventional CBM approach by adding the capability of intelligent condition-based fault diagnosis and the power of predicting the trend of equipment deterioration. An IPDSS model, based on the recurrent neural network (RNN) approach, was developed and tested and run for the critical equipment of a power plant. The results showed that the IPDSS model provided reliable fault diagnosis and strong predictive power for the trend of equipment deterioration. These valuable results could be used as input to an integrated maintenance management system to pre-plan and pre-schedule maintenance work, to reduce inventory costs for spare parts, to cut down unplanned forced outage and to minimise the risk of catastrophic failure.
---
paper_title: On condition based maintenance policy

paper_content:

Abstract In the case of a high-valuable asset, the Operation and Maintenance (O&M) phase requires heavy charges and more efforts than the installation (construction) phase, because it has long usage life and any accident of an asset during this period causes catastrophic damage to an industry. Recently, with the advent of emerging Information Communication Technologies (ICTs), we can get the visibility of asset status information during its usage period. It gives us new challenging issues for improving the efficiency of asset operations. One issue is to implement the Condition-Based Maintenance (CBM) approach that makes a diagnosis of the asset status based on wire or wireless monitored data, predicts the assets abnormality, and executes suitable maintenance actions such as repair and replacement before serious problems happen. In this study, we have addressed several aspects of CBM approach: definition, related international standards, procedure, and techniques with the introduction of some relevant case studies that we have carried out.
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paper_title: A technical framework and roadmap of embedded diagnostics/prognostics for complex mechanical systems in PHM systems

paper_content:

Prognostics and Health Management (PHM) technologies have emerged as a key enabler to provide early indications of system faults and perform predictive maintenance actions. While implementing PHM depends on real-time acquiring the present and future health status of a system accurately. For electronic subsystems, built-in-test (BIT) makes it not difficult to achieve these goals. However, reliable prognostics capability is still a bottle-neck problem for mechanical subsystems due to lack of proper on-line sensors. Recent advancements in sensors and micro-electronics technologies have brought about a novel way out for complex mechanical systems, which is called embedded diagnostics/prognostics (ED/EP). ED/EP can provide real-time present condition and prognostic health state by integrating micro-sensors into mechanical structures during design and manufacture, so ED/EP has a revolutionary progress compared to traditional mechanical fault diagnostic/prognostic ways. But how to study ED/EP for complex mechanical systems has not been focused so far. This paper explores the challenges and needs of efforts to implement ED/EP technologies. In particular, this paper presents a technical framework and roadmap of ED/EP for complex mechanical systems. The framework is proposed based on the methodology of system integration and parallel design, which include six key elements (embedded sensors, embedded sensing design, embedded sensors placement, embedded signals transmission, ED/EP algorithms and embedded self-power). Relationships among these key elements are outlined and they should be considered simultaneously during the design of a complex mechanical system. Technical challenges of each key element are emphasized and existed or potential ways to solve each challenge are summarized in detail. Then the development roadmap of ED/EP in complex mechanical systems is brought forward according to potential advancements in related areas, which can be divided into three different stages: individual technology development, system integration and prototype design, and autonomous mechanical systems. In the end, the presented framework is exemplified with a gearbox
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paper_title: Modelling using UML and BPMN the integration of open reliability, maintenance and condition monitoring management systems: An application in an electric transformer system

paper_content:

Maintenance management of an industrial plant has been always a complex activity. Nowadays Computerized Maintenance Management Systems (CMMSs) help to organize information and thus to carry out maintenance activities in a more efficient way. The emergence of new ICT has increased also the use of Condition Based Maintenance (CBM) systems and the application of Reliability Centred Maintenance (RCM) analysis. Each system is proved to provide benefits to the maintenance management. However when all the systems are adopted, the lack of integration among them can prevent the maximum exploitation of their capabilities. This work aims at fulfilling this gap, proposing an e-maintenance integration platform that combines the features of the three main systems. The methodology and the reference open standards used to develop the platform are exposed. UML-BPMN diagrams represent the emerging algorithms of the designed system. The final product, a software demo is implemented in an electric transformer.
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Title: Applying CBM and PHM concepts with reliability approach for Blowout Preventer (BOP): a literature review

Section 1: INTRODUCTION
Description 1: Write an overview of the significance of operational safety in oil and gas drilling, focusing on the critical role of Blowout Preventer (BOP) systems in preventing blowouts and ensuring well control.

Section 2: BOP reliability and condition monitoring approach
Description 2: Discuss the historical development of BOP reliability studies, the importance of high-quality failure data, and the implementation challenges of BOP condition monitoring and real-time monitoring technology.

Section 3: METHODOLOGY
Description 3: Provide a detailed explanation of the literature review methodology, including the systematic subdivision of the research theme, theoretical framework organization, literature search strategy, and selection criteria.

Section 4: Blowout Preventer System
Description 4: Describe the structure and components of the Blowout Preventer (BOP) system, highlighting the types of preventers, valves, lines, hydraulic connectors, primary control system, and backup system.

Section 5: Reliability concepts
Description 5: Explain the various reliability concepts critical for CBM and PHM, including failure data and analysis, Reliability Centered Maintenance (RCM), and Failure Mode and Effect Analysis (FMEA).

Section 6: Condition Monitoring / Detection
Description 6: Define condition monitoring, its significance, and how it helps in detecting and reporting abnormal events in machinery or systems.

Section 7: Condition-based Maintenance (CBM)
Description 7: Elaborate on the Condition-Based Maintenance (CBM) approach, including its definitions, importance, and implementation challenges.

Section 8: Prognostic Health Management (PHM)
Description 8: Discuss the evolution of CBM into Prognostics and Health Management (PHM), highlighting its capabilities, importance, and reliance on sensor systems for accurate information and prediction of equipment health.

Section 9: Reliability approach for CBM and PHM
Description 9: Explore the relationship between CBM and Reliability Centered Maintenance (RCM), detailing the steps required for developing a CBM framework and integrating it with sensors and monitoring systems for diagnostics and prognostics.

Section 10: CONCLUSIONS
Description 10: Summarize the current state and future prospects of real-time monitoring technology, BOP reliability, and the importance of integrating prognostics and diagnostics in maintenance strategy to enhance operational safety and reduce downtimes.