Pub Date : 2018-08-24DOI: 10.1002/9781119515326.CH13
Saurabh Saxena, Yinjiao Xing, M. Pecht
This chapter presents an overview of the prognostics and systems health management (PHM) techniques used for states estimation and remaining useful life (RUL) prediction of lithium‐ion (Li‐ion) batteries. Li‐ion batteries represent complex electrochemical‐mechanical systems in which various degradation mechanisms are present. State of charge (SOC) and state of health (SOH) provide the estimates of remaining charge and remaining usable capacity of a Li‐ion battery respectively. The chapter discusses methods for battery SOC estimation and also presents a few case studies on experimental data to elaborate these methods. It then presents a case study on SOH estimation and RUL prediction using a Bayesian framework. PHM‐based decision‐making framework for Li‐ion batteries can provide recommendations for mission planning and maintenance scheduling based on the prognostic information, and can control the battery usage in real‐time to optimize the battery life‐cycle performance.
{"title":"PHM of Li-ion Batteries","authors":"Saurabh Saxena, Yinjiao Xing, M. Pecht","doi":"10.1002/9781119515326.CH13","DOIUrl":"https://doi.org/10.1002/9781119515326.CH13","url":null,"abstract":"This chapter presents an overview of the prognostics and systems health management (PHM) techniques used for states estimation and remaining useful life (RUL) prediction of lithium‐ion (Li‐ion) batteries. Li‐ion batteries represent complex electrochemical‐mechanical systems in which various degradation mechanisms are present. State of charge (SOC) and state of health (SOH) provide the estimates of remaining charge and remaining usable capacity of a Li‐ion battery respectively. The chapter discusses methods for battery SOC estimation and also presents a few case studies on experimental data to elaborate these methods. It then presents a case study on SOH estimation and RUL prediction using a Bayesian framework. PHM‐based decision‐making framework for Li‐ion batteries can provide recommendations for mission planning and maintenance scheduling based on the prognostic information, and can control the battery usage in real‐time to optimize the battery life‐cycle performance.","PeriodicalId":163377,"journal":{"name":"Prognostics and Health Management of Electronics","volume":"86 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133586810","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 : 2018-08-24DOI: 10.1002/9781119515326.CH11
M. Pecht, Myeongsu Kang
This chapter develops a kernel‐based learning technique to estimate the health degradation of an electronic circuit due to parametric deviation in the circuit components. A model‐based filtering method is developed for predicting the remaining useful life (RUL) of electronic circuit‐comprising components exhibiting parametric faults. The existing approaches for predicting failures resulting from electronic component parametric faults emphasize identifying monotonically deviating parameters and modeling their progression over time. The existing literature is classified and reviewed based on the approach employed for health estimation and failure prediction ‐ either the component‐centric approach or the circuit‐centric approach. The chapter presents the developed first‐principles‐based model to capture the degradation in circuit performance. It discusses the stochastic algorithm used for joint state‐parameter estimation and RUL prediction. The chapter describes the validation results using data obtained from simulation‐based experiments on the critical circuits of a direct‐current (DC)‐DC converter system.
{"title":"Health and Remaining Useful Life Estimation of Electronic Circuits","authors":"M. Pecht, Myeongsu Kang","doi":"10.1002/9781119515326.CH11","DOIUrl":"https://doi.org/10.1002/9781119515326.CH11","url":null,"abstract":"This chapter develops a kernel‐based learning technique to estimate the health degradation of an electronic circuit due to parametric deviation in the circuit components. A model‐based filtering method is developed for predicting the remaining useful life (RUL) of electronic circuit‐comprising components exhibiting parametric faults. The existing approaches for predicting failures resulting from electronic component parametric faults emphasize identifying monotonically deviating parameters and modeling their progression over time. The existing literature is classified and reviewed based on the approach employed for health estimation and failure prediction ‐ either the component‐centric approach or the circuit‐centric approach. The chapter presents the developed first‐principles‐based model to capture the degradation in circuit performance. It discusses the stochastic algorithm used for joint state‐parameter estimation and RUL prediction. The chapter describes the validation results using data obtained from simulation‐based experiments on the critical circuits of a direct‐current (DC)‐DC converter system.","PeriodicalId":163377,"journal":{"name":"Prognostics and Health Management of Electronics","volume":"7 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114030312","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 : 2018-08-24DOI: 10.1002/9781119515326.CH18
Rhonda D. Walthall, R. Rajamani
This chapter discusses the evolution of maintenance, the goals of the various stakeholders, and the implementation and the application of prognostics and health management (PHM) at commercial airlines from its beginnings to today. While aircraft and aviation are inherently safe, PHM offers an enhancement to safety by enabling component and system failures to be predicted and prevented before the aircraft is in revenue service. PHM enables oil consumption monitoring as well as the monitoring of inflight starting of the auxiliary power unit (APU). Ever since gas turbines have become the major propulsion and auxiliary power systems on aircraft, advanced engine health management (EHM) has become a critical part of the overall PHM landscape as well. Health monitoring of the environmental control system (ECS) is a more recent PHM application at commercial airlines. Aircraft landing system health monitoring is a common practice at commercial airlines and is often referred to as wheel‐and‐brake (WB) monitoring.
{"title":"The Role of PHM at Commercial Airlines","authors":"Rhonda D. Walthall, R. Rajamani","doi":"10.1002/9781119515326.CH18","DOIUrl":"https://doi.org/10.1002/9781119515326.CH18","url":null,"abstract":"This chapter discusses the evolution of maintenance, the goals of the various stakeholders, and the implementation and the application of prognostics and health management (PHM) at commercial airlines from its beginnings to today. While aircraft and aviation are inherently safe, PHM offers an enhancement to safety by enabling component and system failures to be predicted and prevented before the aircraft is in revenue service. PHM enables oil consumption monitoring as well as the monitoring of inflight starting of the auxiliary power unit (APU). Ever since gas turbines have become the major propulsion and auxiliary power systems on aircraft, advanced engine health management (EHM) has become a critical part of the overall PHM landscape as well. Health monitoring of the environmental control system (ECS) is a more recent PHM application at commercial airlines. Aircraft landing system health monitoring is a common practice at commercial airlines and is often referred to as wheel‐and‐brake (WB) monitoring.","PeriodicalId":163377,"journal":{"name":"Prognostics and Health Management of Electronics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124977213","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 : 2018-08-24DOI: 10.1002/9781119515326.CH15
M. Capelli‐Schellpfeffer, Myeongsu Kang, M. Pecht
This chapter introduces the trend of healthcare in the United States, and discusses unique features of healthcare devices and specific safety priorities, as well as clinical priorities related to the devices. It describes benefits of prognostics and health management (PHM) and summarizes the need for PHM in the healthcare devices. The chapter discusses considerations of healthcare devices ‐ such as implantable devices and care bots ‐ with the explanation of unique features of the devices. Unlike engineering venues where PHM is used on installed equipment or complex electronic systems, with implantable medical devices, data collection often occurs where “in situ” is consistent with “surgically embedded inside a patient”. The use of fuses and canaries for PHM presents unanswered questions. Enhanced PHM capabilities will allow detection of failures, avoid catastrophic failures, and prevent damage within the human body as well as for the medical devices.
{"title":"PHM in Healthcare","authors":"M. Capelli‐Schellpfeffer, Myeongsu Kang, M. Pecht","doi":"10.1002/9781119515326.CH15","DOIUrl":"https://doi.org/10.1002/9781119515326.CH15","url":null,"abstract":"This chapter introduces the trend of healthcare in the United States, and discusses unique features of healthcare devices and specific safety priorities, as well as clinical priorities related to the devices. It describes benefits of prognostics and health management (PHM) and summarizes the need for PHM in the healthcare devices. The chapter discusses considerations of healthcare devices ‐ such as implantable devices and care bots ‐ with the explanation of unique features of the devices. Unlike engineering venues where PHM is used on installed equipment or complex electronic systems, with implantable medical devices, data collection often occurs where “in situ” is consistent with “surgically embedded inside a patient”. The use of fuses and canaries for PHM presents unanswered questions. Enhanced PHM capabilities will allow detection of failures, avoid catastrophic failures, and prevent damage within the human body as well as for the medical devices.","PeriodicalId":163377,"journal":{"name":"Prognostics and Health Management of Electronics","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133385209","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 : 2018-08-24DOI: 10.1002/9781119515326.index
{"title":"Index","authors":"","doi":"10.1002/9781119515326.index","DOIUrl":"https://doi.org/10.1002/9781119515326.index","url":null,"abstract":"","PeriodicalId":163377,"journal":{"name":"Prognostics and Health Management of Electronics","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130047286","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 : 2018-08-24DOI: 10.1002/9781119515326.CH3
Shunfeng Cheng, N. Raghavan, Jie Gu, S. Mathew, M. Pecht
{"title":"Physics-of-Failure Approach to PHM","authors":"Shunfeng Cheng, N. Raghavan, Jie Gu, S. Mathew, M. Pecht","doi":"10.1002/9781119515326.CH3","DOIUrl":"https://doi.org/10.1002/9781119515326.CH3","url":null,"abstract":"","PeriodicalId":163377,"journal":{"name":"Prognostics and Health Management of Electronics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129818576","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 : 2018-08-24DOI: 10.1002/9781119515326.CH12
P. Chauhan
{"title":"PHM-Based Qualification of Electronics","authors":"P. Chauhan","doi":"10.1002/9781119515326.CH12","DOIUrl":"https://doi.org/10.1002/9781119515326.CH12","url":null,"abstract":"","PeriodicalId":163377,"journal":{"name":"Prognostics and Health Management of Electronics","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122235695","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 : 2018-08-24DOI: 10.1002/9781119515326.CH14
Moon-Hwan Chang, Jiajie Fan, C. Qian, Bo Sun
{"title":"PHM of Light-Emitting Diodes","authors":"Moon-Hwan Chang, Jiajie Fan, C. Qian, Bo Sun","doi":"10.1002/9781119515326.CH14","DOIUrl":"https://doi.org/10.1002/9781119515326.CH14","url":null,"abstract":"","PeriodicalId":163377,"journal":{"name":"Prognostics and Health Management of Electronics","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124294559","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 : 2018-08-24DOI: 10.1002/9781119515326.CH10
Xin Lei, Amir Kashani-Pour, P. Sandborn, T. Jazouli
{"title":"Valuation and Optimization of PHM-Enabled Maintenance Decisions","authors":"Xin Lei, Amir Kashani-Pour, P. Sandborn, T. Jazouli","doi":"10.1002/9781119515326.CH10","DOIUrl":"https://doi.org/10.1002/9781119515326.CH10","url":null,"abstract":"","PeriodicalId":163377,"journal":{"name":"Prognostics and Health Management of Electronics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129122874","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 : 2018-08-24DOI: 10.1002/9781119515326.CH19
N. J. Jameson, Myeongsu Kang, J. Tian
{"title":"PHM Software for Electronics","authors":"N. J. Jameson, Myeongsu Kang, J. Tian","doi":"10.1002/9781119515326.CH19","DOIUrl":"https://doi.org/10.1002/9781119515326.CH19","url":null,"abstract":"","PeriodicalId":163377,"journal":{"name":"Prognostics and Health Management of Electronics","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116431915","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}
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