{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T15:42:33Z","timestamp":1780587753826,"version":"3.54.1"},"reference-count":36,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51521065"],"award-info":[{"award-number":["51521065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51777154"],"award-info":[{"award-number":["51777154"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Ind. Electron."],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1109\/tie.2020.3009593","type":"journal-article","created":{"date-parts":[[2020,7,21]],"date-time":"2020-07-21T20:36:49Z","timestamp":1595363809000},"page":"8829-8841","source":"Crossref","is-referenced-by-count":120,"title":["A Bayesian Deep Learning RUL Framework Integrating Epistemic and Aleatoric Uncertainties"],"prefix":"10.1109","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4804-5577","authenticated-orcid":false,"given":"Gaoyang","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4738-0210","authenticated-orcid":false,"given":"Li","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0916-0241","authenticated-orcid":false,"given":"Chi-Guhn","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6551-151X","authenticated-orcid":false,"given":"Xiaohua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3977-6298","authenticated-orcid":false,"given":"Mingzhe","family":"Rong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-9326-7"},{"key":"ref32","first-page":"315","article-title":"Deep sparse rectifier neural networks","author":"glorot","year":"0","journal-title":"Proc 14th Int Conf Artif Intell Statist"},{"key":"ref31","first-page":"472","article-title":"Incorporating second-order functional knowledge for better option pricing","author":"dugas","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref30","article-title":"WTTE-RNN: Weibull time to event recurrent neural network","author":"martinsson","year":"2016"},{"key":"ref36","first-page":"359","article-title":"Using dynamic time warping to find patterns in time series","author":"berndt","year":"1994","journal-title":"Proc KDD Workshop"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.02.045"},{"key":"ref34","article-title":"Merging data sources to predict remaining useful life–an automated method to identify prognostic parameters","author":"coble","year":"2010"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2019.2924605"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2014.05.029"},{"key":"ref12","author":"bernardo","year":"2009","journal-title":"Bayesian Theory"},{"key":"ref13","author":"bishop","year":"2006","journal-title":"Pattern Recognition and Machine Learning"},{"key":"ref14","first-page":"5574","article-title":"What uncertainties do we need in bayesian deep learning for computer vision","author":"kendall","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2019.2907440"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2015.2393840"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2684821"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2018.2838078"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2014.2299152"},{"key":"ref28","first-page":"2067","article-title":"Gated feedback recurrent neural networks","author":"chung","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref4","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":"ref27","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2010.11.018"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2016.2586442"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1081\/ETC-200067925"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.11.024"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2733438"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2018.11.027"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2013.2290772"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2018.11.011"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2005.09.012"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2013.07.010"},{"key":"ref22","first-page":"1613","article-title":"Weight uncertainty in neural networks","author":"blundell","year":"0","journal-title":"Proc 32nd Int Conf Mach Learn"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2018.8448804"},{"key":"ref24","author":"weiss","year":"2006","journal-title":"A Course in Probability"},{"key":"ref23","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2007.4383588"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000001","article-title":"Graphical models, exponential families, and variational inference","volume":"1","author":"wainwright","year":"2008","journal-title":"Found Trends Mach Learn"}],"container-title":["IEEE Transactions on Industrial Electronics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/41\/9459470\/09145803.pdf?arnumber=9145803","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:49:13Z","timestamp":1652194153000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9145803\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9]]},"references-count":36,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tie.2020.3009593","relation":{},"ISSN":["0278-0046","1557-9948"],"issn-type":[{"value":"0278-0046","type":"print"},{"value":"1557-9948","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9]]}}}