Pub Date : 2020-11-20DOI: 10.36001/IJPHM.2018.V9I2.2736
A. Soualhi, Y. Hawwari, K. Medjaher, G. Clerc, Razik Hubert, F. Guillet
The reliability and safety of industrial equipments are one of the main objectives of companies to remain competitive in sectors that are more and more exigent in terms of cost and security. Thus, an unexpected shutdown can lead to physical injury as well as economic consequences. This paper aims to show the emergence of the Prognostics and Health Management (PHM) concept in the industry and to describe how it comes to complement the different maintenance strategies. It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring. More specifically, this paper provides a state of the art of existing signal processing techniques that can be used in the PHM strategy. This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes.
{"title":"PHM Survey : Implementation of Signal Processing Methods for Monitoring Bearings and Gearboxes","authors":"A. Soualhi, Y. Hawwari, K. Medjaher, G. Clerc, Razik Hubert, F. Guillet","doi":"10.36001/IJPHM.2018.V9I2.2736","DOIUrl":"https://doi.org/10.36001/IJPHM.2018.V9I2.2736","url":null,"abstract":"The reliability and safety of industrial equipments are one of the main objectives of companies to remain competitive in sectors that are more and more exigent in terms of cost and security. Thus, an unexpected shutdown can lead to physical injury as well as economic consequences. This paper aims to show the emergence of the Prognostics and Health Management (PHM) concept in the industry and to describe how it comes to complement the different maintenance strategies. It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring. More specifically, this paper provides a state of the art of existing signal processing techniques that can be used in the PHM strategy. This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45752947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.36001/IJPHM.2018.V9I1.2696
M. Asgarpour, John Dalsgaard Sørensen
The operation and maintenance costs of offshore wind farms can be significantly reduced if existing corrective actions are performed as efficient as possible and if future corrective actions are avoided by performing sufficient preventive actions. In this paper a prognostic model for degradation monitoring, fault prediction and predictive maintenance of offshore wind components is defined.The diagnostic model defined in this paper is based on degradation, remaining useful lifetime and hybrid inspection threshold models. The defined degradation model is based on an exponential distribution with stochastic scale factor modelled by a normal distribution. Once based on failures, inspection or condition monitoring data sufficient observations on the degradation level of a component are available, using Bayes’ rule and Normal-Normal model prior exponential parameters of the degradation model can be updated. The components of the diagnostic model defined in this paper are further explained within several illustrative examples. At the end, conclusions are given and recommendations for future studies on this topic are discussed.
{"title":"Bayesian based Prognostic Model for Predictive Maintenance of Offshore Wind Farms","authors":"M. Asgarpour, John Dalsgaard Sørensen","doi":"10.36001/IJPHM.2018.V9I1.2696","DOIUrl":"https://doi.org/10.36001/IJPHM.2018.V9I1.2696","url":null,"abstract":"The operation and maintenance costs of offshore wind farms can be significantly reduced if existing corrective actions are performed as efficient as possible and if future corrective actions are avoided by performing sufficient preventive actions. In this paper a prognostic model for degradation monitoring, fault prediction and predictive maintenance of offshore wind components is defined.The diagnostic model defined in this paper is based on degradation, remaining useful lifetime and hybrid inspection threshold models. The defined degradation model is based on an exponential distribution with stochastic scale factor modelled by a normal distribution. Once based on failures, inspection or condition monitoring data sufficient observations on the degradation level of a component are available, using Bayes’ rule and Normal-Normal model prior exponential parameters of the degradation model can be updated. The components of the diagnostic model defined in this paper are further explained within several illustrative examples. At the end, conclusions are given and recommendations for future studies on this topic are discussed.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48618487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.36001/ijphm.2018.v9i2.2732
Matthew Allmark, P. Prickett, R. Grosvenor, Carwyn Frost
The sustainable deployment of Horizontal Axis Tidal Turbines will require effective management and maintenance functions. In part, these can be supported by the engineering of suitable condition monitoring systems. The development of such a system is inevitably challenging, particularly given the present limited level of operational data associated with installed turbines during fault onset. To mitigate this limitation, a computational fluid dynamics model is used to simulate the operational response of a turbine under a known set of fault conditions. Turbine rotor imbalance faults were simulated by the introduction of increasing levels of pitch angle offset for a single turbine blade. The effects of these fault cases upon cyclic variations in the torque developed by the turbine rotor were then used to aid creation of a condition monitoring approach. A parametric tidal turbine rotor model was developed based on the outputs of the computational fluid dynamics models. The model was used to facilitate testing of the condition monitoring approach under a variety of more realistic conditions. The condition monitoring approach showed good performance in fault detection and diagnosis for simulations relating to turbulence intensities of up to 2 %. Finally, the condition monitoring approach was applied to simulations of 10 % turbulence intensity. Under the 10 % turbulence intensity case the rotor monitoring approach was successfully demonstrated in its use for fault detection. The paper closes with discussion of the effectiveness of using computational fluid dynamics simulations extended by parametric models to develop condition monitoring systems for horizontal axis tidal turbine applications.
{"title":"The specification and testing of a Horizontal Axis Tidal Turbine Rotor Monitoring approach","authors":"Matthew Allmark, P. Prickett, R. Grosvenor, Carwyn Frost","doi":"10.36001/ijphm.2018.v9i2.2732","DOIUrl":"https://doi.org/10.36001/ijphm.2018.v9i2.2732","url":null,"abstract":"The sustainable deployment of Horizontal Axis Tidal Turbines will require effective management and maintenance functions. In part, these can be supported by the engineering of suitable condition monitoring systems. The development of such a system is inevitably challenging, particularly given the present limited level of operational data associated with installed turbines during fault onset. To mitigate this limitation, a computational fluid dynamics model is used to simulate the operational response of a turbine under a known set of fault conditions. Turbine rotor imbalance faults were simulated by the introduction of increasing levels of pitch angle offset for a single turbine blade. The effects of these fault cases upon cyclic variations in the torque developed by the turbine rotor were then used to aid creation of a condition monitoring approach. A parametric tidal turbine rotor model was developed based on the outputs of the computational fluid dynamics models. The model was used to facilitate testing of the condition monitoring approach under a variety of more realistic conditions. The condition monitoring approach showed good performance in fault detection and diagnosis for simulations relating to turbulence intensities of up to 2 %. Finally, the condition monitoring approach was applied to simulations of 10 % turbulence intensity. Under the 10 % turbulence intensity case the rotor monitoring approach was successfully demonstrated in its use for fault detection. The paper closes with discussion of the effectiveness of using computational fluid dynamics simulations extended by parametric models to develop condition monitoring systems for horizontal axis tidal turbine applications.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46705143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-17DOI: 10.36001/IJPHM.2017.V8I3.2667
V. Atamuradov, K. Medjaher, P. Dersin, B. Lamoureux, N. Zerhouni
In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations.
{"title":"Prognostics and Health Management for Maintenance Practitioners - Review, Implementation and Tools Evaluation","authors":"V. Atamuradov, K. Medjaher, P. Dersin, B. Lamoureux, N. Zerhouni","doi":"10.36001/IJPHM.2017.V8I3.2667","DOIUrl":"https://doi.org/10.36001/IJPHM.2017.V8I3.2667","url":null,"abstract":"In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43239375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-17DOI: 10.36001/IJPHM.2017.V8I3.2664
Andrea De Martin, A. Dellacasa, G. Jacazio, M. Sorli
Tilting trains are designed to reach high speed on pre-existing railroads without the need of adjusting the tracks geometry or building dedicated lines; the tilting of the carbody keeps an acceptable level of comfort by limiting the lateral acceleration felt by passengers when the train runs along curved tracks with speed higher than the balance speed built into the curve geometry. As such, they are often used to reduce travel times on routes with several curves. Tilting is performed through a position-controlled actuation system which operates according to the commands received from the train control system: in the studied configuration, the torque needed to tilt the car body with respect to the bogie is provided by a series of hydraulic actuators, while the position information used to close the control loop comes from two capacitive sensors located in the front and rear part of each vehicle. Tilt angle measurement is vital for the system operation and for ensuring a safe ride; the traditional solution in case of discrepancy between the signals of the two tilt angle sensors of any vehicle is to disable the tilting function while limiting the train speed to avoid issues during changes of direction. In a similar fashion, the failure in one (or more) of the tilting actuators would result in the loss of the tilting capability and the return to a fixed configuration operating at reduced speed. It should be noticed that the negative impact of the loss of the tilting system is not limited to the faulty train, since it might affect the entire traffic schedule on the interested lines. The paper presents an integrated Health Monitoring framework that makes intelligent use of all available information thus enhancing the system availability, allowing its operation even in presence of faulty sensors and detecting the onset of failures in the actuation system. At the same time its use can facilitate maintenance organization, simplify the spare parts logistics and provide help to the traffic management. The proposed framework has been developed taking advantage of a high-fidelity model of the physical system validated through comparison with experimental mission profiles on the Lichtenfels - Saalfeld and Battipaglia - Reggio Calabria routes, which have been used by the train manufacturer to assess the performance of their tilting trains.
{"title":"Integrated Health Monitoring for the actuation system of high-speed tilting trains","authors":"Andrea De Martin, A. Dellacasa, G. Jacazio, M. Sorli","doi":"10.36001/IJPHM.2017.V8I3.2664","DOIUrl":"https://doi.org/10.36001/IJPHM.2017.V8I3.2664","url":null,"abstract":"Tilting trains are designed to reach high speed on pre-existing railroads without the need of adjusting the tracks geometry or building dedicated lines; the tilting of the carbody keeps an acceptable level of comfort by limiting the lateral acceleration felt by passengers when the train runs along curved tracks with speed higher than the balance speed built into the curve geometry. As such, they are often used to reduce travel times on routes with several curves. Tilting is performed through a position-controlled actuation system which operates according to the commands received from the train control system: in the studied configuration, the torque needed to tilt the car body with respect to the bogie is provided by a series of hydraulic actuators, while the position information used to close the control loop comes from two capacitive sensors located in the front and rear part of each vehicle. Tilt angle measurement is vital for the system operation and for ensuring a safe ride; the traditional solution in case of discrepancy between the signals of the two tilt angle sensors of any vehicle is to disable the tilting function while limiting the train speed to avoid issues during changes of direction. In a similar fashion, the failure in one (or more) of the tilting actuators would result in the loss of the tilting capability and the return to a fixed configuration operating at reduced speed. It should be noticed that the negative impact of the loss of the tilting system is not limited to the faulty train, since it might affect the entire traffic schedule on the interested lines. The paper presents an integrated Health Monitoring framework that makes intelligent use of all available information thus enhancing the system availability, allowing its operation even in presence of faulty sensors and detecting the onset of failures in the actuation system. At the same time its use can facilitate maintenance organization, simplify the spare parts logistics and provide help to the traffic management. The proposed framework has been developed taking advantage of a high-fidelity model of the physical system validated through comparison with experimental mission profiles on the Lichtenfels - Saalfeld and Battipaglia - Reggio Calabria routes, which have been used by the train manufacturer to assess the performance of their tilting trains.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42892990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-17DOI: 10.36001/IJPHM.2018.V9I1.2670
F. Cannarile, P. Baraldi, P. Colombo, E. Zio
Sensor data validation has become an important issue in the operation and control of energy production plants. An undetected sensor malfunction may convey inaccurate or misleading information about the actual plant state, possibility leading to unnecessary downtimes and, consequently, large financial losses. The objective of this work is the development of a novel sensor data validation method to promptly detect sensor malfunctions. The proposed method is based on the analysis of data regularity properties, through the joint use of Continuous Wavelet Transform and image analysis techniques. Differently from the typical sensor data validation techniques which detect a sensor malfunction by observing variations in the relationships among measurements provided by different sensors, the proposed method validates the data collected by a given sensor only using historical data collected from the sensor itself. The proposed method is shown able to correctly detect different types and intensities of sensor malfunctions from energy production plants.
{"title":"A Novel Method for Sensor Data Validation based on the analysis of Wavelet Transform Scalograms","authors":"F. Cannarile, P. Baraldi, P. Colombo, E. Zio","doi":"10.36001/IJPHM.2018.V9I1.2670","DOIUrl":"https://doi.org/10.36001/IJPHM.2018.V9I1.2670","url":null,"abstract":"Sensor data validation has become an important issue in the operation and control of energy production plants. An undetected sensor malfunction may convey inaccurate or misleading information about the actual plant state, possibility leading to unnecessary downtimes and, consequently, large financial losses. The objective of this work is the development of a novel sensor data validation method to promptly detect sensor malfunctions. The proposed method is based on the analysis of data regularity properties, through the joint use of Continuous Wavelet Transform and image analysis techniques. Differently from the typical sensor data validation techniques which detect a sensor malfunction by observing variations in the relationships among measurements provided by different sensors, the proposed method validates the data collected by a given sensor only using historical data collected from the sensor itself. The proposed method is shown able to correctly detect different types and intensities of sensor malfunctions from energy production plants.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44630976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-13DOI: 10.36001/IJPHM.2016.V7I3.2413
Patrick T. Hester, Andrew J. Collins, B. Ezell, J. Horst
Successful use of prognostics involves the prediction of future system behaviors in an effort to maintain system availability and reduce the cost of maintenance and repairs. Recent work by the National Institute of Standards and Technology indicates that the field of prognostics and health management is vital for remaining competitive in today’s manufacturing environment. While prognostics-based maintenance involves many traditional operations researchcentric challenges for successful deployment such as limited availability of information and concerns regarding computational efficiency, the authors argue in this paper that the field of prognostics and health management, still in its embryonic development stage, could benefit greatly from considering soft operations research techniques as well. Specifically, the authors propose the use of qualitative problem structuring techniques that aid in problem understanding and scoping. This paper provides an overview of these soft methods and discusses and demonstrates how manufacturers might use them. An approach combining problem structuring methods with traditional operations research techniques would help accelerate the development of the prognostics field.
{"title":"A Review of Problem Structuring Methods for Consideration in Prognostics and Smart Manufacturing","authors":"Patrick T. Hester, Andrew J. Collins, B. Ezell, J. Horst","doi":"10.36001/IJPHM.2016.V7I3.2413","DOIUrl":"https://doi.org/10.36001/IJPHM.2016.V7I3.2413","url":null,"abstract":"Successful use of prognostics involves the prediction of future system behaviors in an effort to maintain system availability and reduce the cost of maintenance and repairs. Recent work by the National Institute of Standards and Technology indicates that the field of prognostics and health management is vital for remaining competitive in today’s manufacturing environment. While prognostics-based maintenance involves many traditional operations researchcentric challenges for successful deployment such as limited availability of information and concerns regarding computational efficiency, the authors argue in this paper that the field of prognostics and health management, still in its embryonic development stage, could benefit greatly from considering soft operations research techniques as well. Specifically, the authors propose the use of qualitative problem structuring techniques that aid in problem understanding and scoping. This paper provides an overview of these soft methods and discusses and demonstrates how manufacturers might use them. An approach combining problem structuring methods with traditional operations research techniques would help accelerate the development of the prognostics field.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2020-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49327214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-13DOI: 10.36001/IJPHM.2016.V7I2.2404
I. Jennions, O. Niculita, M. Esperon-Miguez
Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable effective and efficient maintenance and operation of the target vehicle. It accounts for the collecting of data, conducting analysis, and supporting the decision-making process for sustainment and operation. The design of IVHM systems endeavours to account for all causes of failure in a disciplined, systems engineering, manner. With industry striving to reduce through-life cost, IVHM is a powerful tool to give forewarning of impending failure and hence control over the outcome. Benefits have been realised from this approach across a number of different sectors but, hindering our ability to realise further benefit from this maturing technology, is the fact that IVHM is still treated as added on to the design of the asset, rather than being a sub-system in its own right, fully integrated with the asset design. The elevation and integration of IVHM in this way will enable architectures to be chosen that accommodate health ready sub-systems from the supply chain and design trade-offs to be made, to name but two major benefits. Barriers to IVHM being integrated with the asset design are examined in this paper. The paper presents progress in overcoming them, and suggests potential solutions for those that remain. It addresses the IVHM system design from a systems engineering perspective and the integration with the asset design will be described within an industrial design process.
{"title":"Integrating IVHM and Asset Design","authors":"I. Jennions, O. Niculita, M. Esperon-Miguez","doi":"10.36001/IJPHM.2016.V7I2.2404","DOIUrl":"https://doi.org/10.36001/IJPHM.2016.V7I2.2404","url":null,"abstract":"Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable effective and efficient maintenance and operation of the target vehicle. It accounts for the collecting of data, conducting analysis, and supporting the decision-making process for sustainment and operation. The design of IVHM systems endeavours to account for all causes of failure in a disciplined, systems engineering, manner. With industry striving to reduce through-life cost, IVHM is a powerful tool to give forewarning of impending failure and hence control over the outcome. Benefits have been realised from this approach across a number of different sectors but, hindering our ability to realise further benefit from this maturing technology, is the fact that IVHM is still treated as added on to the design of the asset, rather than being a sub-system in its own right, fully integrated with the asset design. The elevation and integration of IVHM in this way will enable architectures to be chosen that accommodate health ready sub-systems from the supply chain and design trade-offs to be made, to name but two major benefits. Barriers to IVHM being integrated with the asset design are examined in this paper. The paper presents progress in overcoming them, and suggests potential solutions for those that remain. It addresses the IVHM system design from a systems engineering perspective and the integration with the asset design will be described within an industrial design process.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2020-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44360864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-11DOI: 10.36001/IJPHM.2016.V7I2.2362
C. Sankavaram, A. Kodali, K. Pattipati, Satnam Singh, Yilu Zhang, M. Salman
This paper presents a unified data-driven prognostic framework that combines failure time data, static parameter data and dynamic time-series data. The framework employs proportional hazards model and a soft dynamic multiple fault diagnosis algorithm for inferring the degraded state trajectories of components and to estimate their remaining useful life times. The framework takes into account the cross-subsystem fault propagation, a case prevalent in any networked and embedded system. The key idea is to use Cox proportional hazards model to estimate the survival functions of error codes and symptoms (probabilistic test outcomes/prognostic indicators) from failure time data and static parameter data, and use them to infer the survival functions of components via soft dynamic multiple fault diagnosis algorithm. The average remaining useful life and its higher-order central moments (e.g., variance, skewness, kurtosis) can be estimated from these component survival functions. The framework is demonstrated on datasets derived from two automotive systems, namely hybrid electric vehicle regenerative braking system, and an electronic throttle control subsystem simulator. Although the proposed framework is validated on automotive systems, it has the potential to be applicable to a wide variety of systems, ranging from aerospace systems to buildings to power grids.
{"title":"An Inference-based Prognostic Framework for Health Management of Automotive Systems","authors":"C. Sankavaram, A. Kodali, K. Pattipati, Satnam Singh, Yilu Zhang, M. Salman","doi":"10.36001/IJPHM.2016.V7I2.2362","DOIUrl":"https://doi.org/10.36001/IJPHM.2016.V7I2.2362","url":null,"abstract":"This paper presents a unified data-driven prognostic framework that combines failure time data, static parameter data and dynamic time-series data. The framework employs proportional hazards model and a soft dynamic multiple fault diagnosis algorithm for inferring the degraded state trajectories of components and to estimate their remaining useful life times. The framework takes into account the cross-subsystem fault propagation, a case prevalent in any networked and embedded system. The key idea is to use Cox proportional hazards model to estimate the survival functions of error codes and symptoms (probabilistic test outcomes/prognostic indicators) from failure time data and static parameter data, and use them to infer the survival functions of components via soft dynamic multiple fault diagnosis algorithm. The average remaining useful life and its higher-order central moments (e.g., variance, skewness, kurtosis) can be estimated from these component survival functions. The framework is demonstrated on datasets derived from two automotive systems, namely hybrid electric vehicle regenerative braking system, and an electronic throttle control subsystem simulator. Although the proposed framework is validated on automotive systems, it has the potential to be applicable to a wide variety of systems, ranging from aerospace systems to buildings to power grids.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2020-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42511514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-03DOI: 10.36001/IJPHM.2015.V6I3.2267
S. Al-Dahidi, F. Maio, P. Baraldi, E. Zio, R. Seraoui
The objective of the present work is to develop a novel approach for combining in an ensemble multiple base clusterings of operational transients of industrial equipment, when the number of clusters in the final consensus clustering is unknown. A measure of pairwise similarity is used to quantify the co-association matrix that describes the similarity among the different base clusterings. Then, a Spectral Clustering technique of literature, embedding the unsupervised K-Means algorithm, is applied to the coassociation matrix for finding the optimum number of clusters of the final consensus clustering, based on Silhouette validity index calculation. The proposed approach is developed with reference to an artificial casestudy, properly designed to mimic the signal trend behavior of a Nuclear Power Plant (NPP) turbine during shut-down. The results of the artificial case have been compared with those achieved by a state-of-art approach, known as Clusterbased Similarity Partitioning and Serial Graph Partitioning and Fill-reducing Matrix Ordering Algorithms (CSPAMETIS). The comparison shows that the proposed approach is able to identify a final consensus clustering that classifies the transients with better accuracy and robustness compared to the CSPA-METIS approach. The approach is, then, validated on an industrial case concerning 149 shut-down transients of a NPP turbine.
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