{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T07:20:35Z","timestamp":1790752835015,"version":"4.1.0"},"reference-count":286,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Rel."],"published-print":{"date-parts":[[2020,9]]},"DOI":"10.1109\/tr.2019.2957965","type":"journal-article","created":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T15:47:55Z","timestamp":1577461675000},"page":"1110-1129","source":"Crossref","is-referenced-by-count":176,"title":["A Review on Prognostics Methods for Engineering Systems"],"prefix":"10.1109","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4099-6298","authenticated-orcid":false,"given":"Jian","family":"Guo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2673-9909","authenticated-orcid":false,"given":"Zhaojun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meiyan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref275","first-page":"397","article-title":"System-level prognostics for national airspace","author":"daigle","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref274","first-page":"71","article-title":"A distributed approach system on system-level prognostics","author":"daigle","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref277","doi-asserted-by":"publisher","DOI":"10.1002\/nav.21583"},{"key":"ref276","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2016.05.006"},{"key":"ref271","doi-asserted-by":"publisher","DOI":"10.1201\/b19094-97"},{"key":"ref270","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.10.046"},{"key":"ref273","first-page":"576","article-title":"Standards for prognostics and health management (PHM) techniques within manufacturing operations","author":"vogl","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-7439(00)00058-7"},{"key":"ref272","article-title":"Adaptive multi-scale prognostics and health management for smart manufacturing systems","volume":"7","author":"choo","year":"2016","journal-title":"Int Conf Prognostics Health Manage"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2017.09.003"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2015.05.004"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.1016\/j.conengprac.2017.12.006"},{"key":"ref173","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodqual.2014.05.005"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.1007\/BF00140873"},{"key":"ref175","first-page":"229","author":"sklar","year":"1959","journal-title":"Fonctions de répartition à n dimensions et leurs marges"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1198\/016214502388618942"},{"key":"ref177","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/86.3.677"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2017.7998299"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-0409-4"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.3182\/20090610-3-IT-4004.00049"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/IEEM.2009.5372973"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2012.11.146"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhydene.2014.05.005"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2009.08.001"},{"key":"ref30","article-title":"Data fusion for enhanced aircraft engine prognostics and health management","author":"volponi","year":"0"},{"key":"ref267","doi-asserted-by":"publisher","DOI":"10.1109\/COASE.2009.5234108"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2007.07.004"},{"key":"ref268","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2017.2693821"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2007.07.003"},{"key":"ref269","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2017.04.005"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s12206-014-1222-z"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICQR2MSE.2012.6246322"},{"key":"ref286","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2011.2182221"},{"key":"ref285","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2015.2455055"},{"key":"ref284","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2016.2570568"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.1109\/SDPC.2017.110"},{"key":"ref283","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2012.02.015"},{"key":"ref180","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asr068"},{"key":"ref282","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2011.10.009"},{"key":"ref281","doi-asserted-by":"publisher","DOI":"10.1109\/IEEM.2009.5372976"},{"key":"ref280","doi-asserted-by":"publisher","DOI":"10.5391\/IJFIS.2007.7.4.221"},{"key":"ref185","doi-asserted-by":"publisher","DOI":"10.1109\/PVSC.2014.6925661"},{"key":"ref184","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.02.024"},{"key":"ref183","first-page":"18","article-title":"Spatio-temporal modeling of degradation processes through stochastic Gamma and Gaussian processes","author":"oumouni","year":"2017","journal-title":"Proc 2nd Int Conf Sci Technol"},{"key":"ref182","doi-asserted-by":"publisher","DOI":"10.1080\/00224065.2018.1436833"},{"key":"ref189","doi-asserted-by":"publisher","DOI":"10.1006\/mssp.2000.1309"},{"key":"ref188","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2013.09.016"},{"key":"ref187","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2012.08.033"},{"key":"ref186","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2007.05.008"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1080\/10255810215021"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3390\/en7128076"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006-341X.2003.00089.x"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/NAFIPS.2005.1548499"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1002\/9780470117842"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.23919\/ECC.2009.7074633"},{"key":"ref21","article-title":"A robust & reliable data-driven prognostics approach based on extreme learning machine and fuzzy clustering","author":"javed","year":"2014"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1006\/mssp.2001.1396"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-009-0356-9"},{"key":"ref278","article-title":"Prognostics and health management for multi-component systems","author":"assaf","year":"2018"},{"key":"ref26","first-page":"121","article-title":"An adaptive recurrent neural network for remaining useful life prediction of Lithium-ion batteries","author":"liu","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref279","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2014.2299152"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2007.910302"},{"key":"ref50","first-page":"727","article-title":"Feature extraction and ensemble decision tree classifier in plant failure detection","author":"xie","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.5772\/7429"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2014.2299151"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2010.2040761"},{"key":"ref156","author":"rasmussen","year":"2006","journal-title":"gaussian process for machine learning"},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.1177\/1748006X15579322"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2006.876609"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1080\/15732470601012154"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2008.2011659"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.04.069"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-54030-5"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.2307\/1269661"},{"key":"ref149","doi-asserted-by":"publisher","DOI":"10.1080\/002075400188933"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.08.049"},{"key":"ref58","first-page":"211","article-title":"Sparse Bayesian learning and the relevance vector machine","volume":"1","author":"tipping","year":"2001","journal-title":"J Mach Learn Res"},{"key":"ref57","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.ymssp.2012.02.015","article-title":"Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine","volume":"32","author":"pham","year":"2012","journal-title":"Mech Syst Signal Process"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1177\/1687814016685963"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1177\/0954406215590167"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2013.2285318"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2009.10.011"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.12.027"},{"key":"ref167","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2015.07.022"},{"key":"ref166","first-page":"1","article-title":"Review of functional data analysis","author":"wang","year":"2015","journal-title":"Ann Statist"},{"key":"ref165","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000001745"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1214\/009053605000000660"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1007\/b98888"},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1007\/b98886"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfranklin.2016.06.039"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2016.07.039"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-44742-1"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1002\/9780470385845"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1177\/1687814015627769"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-009-2482-0"},{"key":"ref8","first-page":"427","article-title":"Uncertainty representation and interpretation in model-based prognostics algorithms based on Kalman Filter estimation","author":"celaya","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2017.05.004"},{"key":"ref7","article-title":"Introduction to prognostics","author":"goebel","year":"0","journal-title":"Proc Eur PHM Conf"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/S0933-3657(00)00053-1"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1016\/j.pnucene.2014.08.006"},{"key":"ref9","first-page":"1","article-title":"Uncertainty in prognostics and systems health management","volume":"6","author":"sankararaman","year":"2015","journal-title":"Int Conf Prognostics Health Manage"},{"key":"ref158","doi-asserted-by":"publisher","DOI":"10.1039\/C7RA03959J"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1080\/24725854.2016.1263771"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2015.01.164"},{"key":"ref48","first-page":"205","article-title":"Repairable system reliability: Recent developments in CBM optimization","volume":"4","author":"jardine","year":"2008","journal-title":"Int J Performability Eng"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1057\/palgrave.jors.2602058"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.microrel.2013.03.010"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2007.00758.x"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1080\/0740817X.2013.876126"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2014.09.014"},{"key":"ref73","first-page":"857","article-title":"Fault prognostics using dynamic wavelet neural networks","author":"vachtsevanos","year":"0","journal-title":"Proc IEEE Syst Readiness Technol Conf"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060401154089"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1016\/0378-4266(94)90007-8"},{"key":"ref70","first-page":"727","article-title":"Feature extraction and ensemble decision tree classifier in plant failure detection","author":"xie","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1016\/j.camwa.2010.03.065"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1007\/s11771-015-3013-9"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2005.11.008"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2006.886368"},{"key":"ref78","first-page":"107","article-title":"Feature fusion and degradation using self-organizing map","author":"qiu","year":"0","journal-title":"Proc Int Conf Mach Learn Appl"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2004.08.001"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2014.11.031"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2012.01.023"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/11744078_10"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.02.199"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2012.03.011"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2013.03.129"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2012.6299511"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1145\/2133360.2133363"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2019.8819409"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1115\/IMECE2018-87623"},{"key":"ref197","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-005-0111-0"},{"key":"ref198","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2006.01.041"},{"key":"ref199","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2006.10.001"},{"key":"ref193","doi-asserted-by":"publisher","DOI":"10.1080\/00207540412331327727"},{"key":"ref194","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2012.2194175"},{"key":"ref195","doi-asserted-by":"publisher","DOI":"10.1201\/b17399-166"},{"key":"ref196","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2009.11.011"},{"key":"ref95","article-title":"Remaining useful life estimation for systems with abrupt failures","volume":"10","author":"huang","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref94","article-title":"Recurrent neural networks for online remaining useful life estimation in ion mill etching system","volume":"10","author":"tv","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref190","first-page":"339","article-title":"State and model parameter estimation for transmissions on heavy hauler trucks using oil data","author":"lin","year":"0","journal-title":"Proc COMADEM"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2017.7998311"},{"key":"ref191","doi-asserted-by":"publisher","DOI":"10.1239\/aap\/1046366106"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2805189"},{"key":"ref192","doi-asserted-by":"publisher","DOI":"10.1057\/palgrave.jors.2602185"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/PHM-Chongqing.2018.00184"},{"key":"ref90","article-title":"User's guide for the commercial modular aero-propulsion system simulation (C-MAPSS)","author":"liu","year":"2012"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsv.2016.05.027"},{"key":"ref99","first-page":"214","article-title":"Deep convolutional neural network based regression approach for estimation of remaining useful life","author":"babu","year":"0","journal-title":"Proc Int Conf Database Syst Adv Appl"},{"key":"ref96","article-title":"Learning longer memory in recurrent neural networks","author":"mikolov","year":"0","journal-title":"Proc Int Conf Learni Represent Workshop Track"},{"key":"ref97","article-title":"Recent advances in recurrent neural networks","author":"salehinejad","year":"2017"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.05.050"},{"key":"ref81","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref84","first-page":"1","article-title":"Recurrent neural networks for remaining useful life estimation","author":"helmes","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2018.11.027"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsv.2005.03.007"},{"key":"ref89","article-title":"PHM08 Challenge Data Set","author":"saxena","year":"2008","journal-title":"NASA Ames Prognostics Data Repository NASA Ames Research Center"},{"key":"ref85","first-page":"353","article-title":"Combining a recurrent neural network and a PID controller for prognostic purpose: A way to improve the accuracy of predictions","volume":"5","author":"zemouri","year":"2010","journal-title":"WSEAS Trans Syst Control"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2010.2078296"},{"key":"ref87","first-page":"1","article-title":"Predicting remaining useful life using time series embeddings based on recurrent neural networks","author":"gugulothu","year":"0","journal-title":"Proc ACM SIGKDD Workshop Mach Learn Prognostics Health Manage"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.02.045"},{"key":"ref200","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-008-0111-4"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2017.11.021"},{"key":"ref209","first-page":"167","article-title":"Dynamic Bayesian networks for prognostics","author":"bartram","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref203","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2723943"},{"key":"ref204","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-017-0916-7"},{"key":"ref201","doi-asserted-by":"publisher","DOI":"10.1002\/qre.1469"},{"key":"ref202","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/278120"},{"key":"ref207","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.07.050"},{"key":"ref208","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2011.10.018"},{"key":"ref205","article-title":"A critical review of recurrent neural networks of sequence learning","author":"lipton","year":"2015"},{"key":"ref206","doi-asserted-by":"publisher","DOI":"10.1007\/s12204-008-0318-y"},{"key":"ref211","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001401000836"},{"key":"ref210","article-title":"System health diagnosis and prognosis using dynamic Bayesian networks","author":"bartram","year":"2013"},{"key":"ref212","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2014.2323212"},{"key":"ref213","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2017.01.073"},{"key":"ref214","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression models and life-table (with discussion)","volume":"34","author":"cox","year":"1972","journal-title":"J Royal Statist Soc Series B (Methodol )"},{"key":"ref215","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2013.10.013"},{"key":"ref216","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2012.02.015"},{"key":"ref217","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2288-13-33"},{"key":"ref218","article-title":"A physics-based degradation modeling framework for diagnostic and prognostic studies in electrolytic capacitors","author":"kulkarni","year":"2013"},{"key":"ref219","first-page":"84","article-title":"A model-based prognostics approach applied to pneumatic valve","volume":"2","author":"daigle","year":"2011","journal-title":"Int Conf Prognostics Health Manage"},{"key":"ref220","doi-asserted-by":"publisher","DOI":"10.1109\/TDMR.2011.2157695"},{"key":"ref222","doi-asserted-by":"publisher","DOI":"10.1109\/PHM.2008.4711438"},{"key":"ref221","doi-asserted-by":"publisher","DOI":"10.1115\/ICONE10-22458"},{"key":"ref229","doi-asserted-by":"publisher","DOI":"10.1006\/mssp.2000.1301"},{"key":"ref228","doi-asserted-by":"publisher","DOI":"10.1006\/mssp.1998.0183"},{"key":"ref227","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1016\/j.ress.2011.07.007","article-title":"A probabilistic physics-of-failure model for prognostic health management of structure subject to pitting and corrosion-fatigue","volume":"96","author":"cookah","year":"2011","journal-title":"Rel Eng Syst Saf"},{"key":"ref226","article-title":"How to measure lifetime for robustness validation-step by step","author":"winter","year":"2012","journal-title":"ZVEI - German Electrical and Electronic Manufacturers Association"},{"key":"ref225","doi-asserted-by":"publisher","DOI":"10.1109\/JESTPE.2013.2290282"},{"key":"ref224","doi-asserted-by":"publisher","DOI":"10.1109\/TPEL.2014.2346485"},{"key":"ref223","doi-asserted-by":"publisher","DOI":"10.1109\/TDMR.2011.2157695"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1109\/PHM.2010.5414591"},{"key":"ref126","author":"boufenar","year":"2012","journal-title":"Condition Monitoring of Machinery in Non-Stationary Operations"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1016\/j.triboint.2015.12.037"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2016.06.024"},{"key":"ref129","article-title":"Automatic feature generation and selection in predictive analytics solution","author":"den bosch","year":"2017"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2009.21"},{"key":"ref130","first-page":"3581","article-title":"Data-driven neural network methodolgy to remaining life predictions for Aircraft actuator components","author":"byington","year":"0","journal-title":"Proc IEEE Aerosp Conf"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2014.2315912"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.4249\/scholarpedia.6043"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2014.10.014"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2009.08.001"},{"key":"ref232","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2015.02.008"},{"key":"ref233","doi-asserted-by":"publisher","DOI":"10.1016\/j.protcy.2016.03.015"},{"key":"ref230","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2004.06.007"},{"key":"ref231","doi-asserted-by":"publisher","DOI":"10.1109\/87.508893"},{"key":"ref239","first-page":"1","article-title":"Modeling Li-ion battery capacity depletion in a particle filtering framework","author":"saha","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref238","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2011.08.040"},{"key":"ref235","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2014.12.003"},{"key":"ref234","doi-asserted-by":"publisher","DOI":"10.1007\/s11837-004-0029-2"},{"key":"ref237","doi-asserted-by":"publisher","DOI":"10.1006\/mssp.2000.1301"},{"key":"ref236","doi-asserted-by":"publisher","DOI":"10.1006\/mssp.2002.1483"},{"key":"ref136","first-page":"1","article-title":"Fuzzy neural network in condition maintenance for marine electric propulsion system, Published in: Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)","author":"liang","year":"0","journal-title":"Proc IEEE Conf & Exp"},{"key":"ref135","first-page":"1","article-title":"Fuzzy neural network modelling for tool wear estimation in dry milling operation","author":"li","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2014.2378056"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1016\/j.fss.2015.09.001"},{"key":"ref139","first-page":"1","article-title":"Naive Bayesian classifier for on-line remaining useful life prediction of degrading bearings","author":"maio","year":"0","journal-title":"Proc MM"},{"key":"ref140","first-page":"836","article-title":"Prognosis of rolling bearing failure based on regression analysis and fuzzy logic","author":"jantunen","year":"0","journal-title":"VETOMAC-3 and ACSIM-2004"},{"key":"ref141","author":"kennedy","year":"2001","journal-title":"Swarm Intelligence"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2010.2100130"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2013.2276473"},{"key":"ref2","article-title":"ISO 13381-1","year":"2015","journal-title":"Condition Monitoring and Diagnostics of Machines - Prognostics-Part1 General Guidelines"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.1016\/j.microrel.2013.01.006"},{"key":"ref1","article-title":"Introduction to prognostics","author":"clements","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2014.04.077"},{"key":"ref241","doi-asserted-by":"publisher","DOI":"10.1177\/1687814016664660"},{"key":"ref242","first-page":"1","article-title":"Physics based methodology for wind turbine failure detection, diagnostics and prognostics","author":"breteler","year":"0","journal-title":"Proc Eur Wind Energy Assoc Annu Conf Exhib"},{"key":"ref243","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-009-0340-4"},{"key":"ref244","doi-asserted-by":"publisher","DOI":"10.1115\/1.2833137"},{"key":"ref240","first-page":"363","article-title":"An energy-based prognostic framework to predict fatigue damage evolution in composites","author":"chiachio","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref248","doi-asserted-by":"publisher","DOI":"10.1214\/14-STS511"},{"key":"ref247","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2015.11.008"},{"key":"ref246","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2013.02.019"},{"key":"ref245","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2010.11.018"},{"key":"ref249","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2011.08.002"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2017.2697842"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2016.2586442"},{"key":"ref107","first-page":"223","article-title":"Device health estimation by combining contextual control information with sensor data and device health prognostics utilizing restricted Boltzmann machine","author":"liao","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.3390\/app8122416"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2907131"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref102","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"0"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966269"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2013.02.022"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2017.02.013"},{"key":"ref250","first-page":"353","article-title":"An unscented Kalman filter based approach for the health monitoring, and prognostics of a polymer electrolyte membrane fuel cell","author":"zhang","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref251","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2004.02.032"},{"key":"ref254","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2008.08.103"},{"key":"ref255","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2011.03.059"},{"key":"ref252","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2006.06.004"},{"key":"ref253","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2007.03.072"},{"key":"ref257","first-page":"17981","article-title":"Extended Kalman filtering for remaining-useful-life estimation of bearings","volume":"62","author":"rodney","year":"2015","journal-title":"IEEE Trans Ind Electron Soc"},{"key":"ref256","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2012.2220698"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2005.09.012"},{"key":"ref259","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2016.10.026"},{"key":"ref11","first-page":"107","article-title":"A survey of artificial intelligence for prognostics","author":"schwabacher","year":"0","journal-title":"Proc AAAI Fall Symp"},{"key":"ref258","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2015.11.071"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/793161"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2010.11.018"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2014.02.006"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-016-0019-3"},{"key":"ref16","first-page":"148","article-title":"Major challenges in prognostics: Study on benchmarking prognostics datasets","author":"eker","year":"0","journal-title":"Proc Eur PHM Conf"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1109\/ISPASS.2019.00041"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2008.06.009"},{"key":"ref117","article-title":"Learning to monitor machine health with convolutional Bi-directional LSTM networks","volume":"17","author":"zhao","year":"2017","journal-title":"SENSORS"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1002\/9781119371052"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2014.09.014"},{"key":"ref119","first-page":"5574","article-title":"What uncertainties do we need in Bayesian deep learning for computer vision?","author":"kendall","year":"0","journal-title":"Proc 31st Conf Neural Inf Process Syst"},{"key":"ref114","first-page":"477","article-title":"Bearing health condition prediction using deep belief network","author":"zhao","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/9306205"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.3390\/app7070649"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2582798"},{"key":"ref120","first-page":"7482","article-title":"Multi-task learning using uncertainty to weigh losses for scene geometry and semantics","author":"kendall","year":"0","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.2018.1527727"},{"key":"ref122","article-title":"Convergence of edge computing and deep learning: A comprehensive survey","author":"han","year":"2019"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.3390\/s18072110"},{"key":"ref260","doi-asserted-by":"publisher","DOI":"10.1109\/ASSPCC.2000.882463"},{"key":"ref261","doi-asserted-by":"publisher","DOI":"10.1016\/j.microrel.2012.11.010"},{"key":"ref262","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2013.07.061"},{"key":"ref263","doi-asserted-by":"publisher","DOI":"10.1016\/j.microrel.2012.12.004"},{"key":"ref264","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2015.07.013"},{"key":"ref265","first-page":"3476","article-title":"A hybrid prognostics methodology for electronic products","author":"kumar","year":"0","journal-title":"Proc IEEE Int Joint Conf Neural Netw (IEEE World Congr Comput Intell )"},{"key":"ref266","first-page":"102","article-title":"A fusion prognostics method for remaining useful life prediction of eletronic products","author":"cheng","year":"2009","journal-title":"Proc 5th Annu IEEE Conf Autom Sci Eng"}],"container-title":["IEEE Transactions on Reliability"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/24\/9181670\/08944164.pdf?arnumber=8944164","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T03:29:54Z","timestamp":1722223794000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8944164\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9]]},"references-count":286,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tr.2019.2957965","relation":{},"ISSN":["0018-9529","1558-1721"],"issn-type":[{"value":"0018-9529","type":"print"},{"value":"1558-1721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9]]}}}