{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T22:59:17Z","timestamp":1769727557800,"version":"3.49.0"},"reference-count":30,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2017,10,1]],"date-time":"2017-10-01T00:00:00Z","timestamp":1506816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Ind. Electron."],"published-print":{"date-parts":[[2017,10]]},"DOI":"10.1109\/tie.2017.2701790","type":"journal-article","created":{"date-parts":[[2017,5,5]],"date-time":"2017-05-05T18:25:41Z","timestamp":1494008741000},"page":"8158-8166","source":"Crossref","is-referenced-by-count":37,"title":["Uncertainty Management in Lebesgue-Sampling-Based Diagnosis and Prognosis for Lithium-Ion Battery"],"prefix":"10.1109","volume":"64","author":[{"given":"Wuzhao","family":"Yan","sequence":"first","affiliation":[]},{"given":"Bin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Guangquang","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"John","family":"Weddington","sequence":"additional","affiliation":[]},{"given":"Guangxing","family":"Niu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref30","first-page":"1","article-title":"Metrics for offline evaluation of prognostic performance","volume":"1","author":"saxena","year":"2010","journal-title":"Int Conf Prognostics Health Manage"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/AERO.2009.4839668"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/AERO.2013.6496971"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.2514\/1.C000279"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2012.2183834"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/PHM.2008.4711433"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.2514\/6.2012-2422"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2014.2313801"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/AERO.2008.4526631"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.179"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TSMCA.2003.809222","article-title":"Computationally efficient algorithms for multiple fault diagnosis in large graph-based systems","volume":"33","author":"tu","year":"2003","journal-title":"IEEE Trans Syst Man Cybern A Syst Humans"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2013.2276473"},{"key":"ref4","first-page":"1","article-title":"Data-driven prognostics for lithium-ion battery based on Gaussian process regression","author":"liu","year":"0","journal-title":"Proc IEEE Prognostics Syst Health Manage Conf"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1002\/9780470117842"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2012.2215142"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/PESGM.2016.7741980"},{"key":"ref29","first-page":"1","article-title":"Parameters adaption of Lebesgue sampling-based diagnosis and prognosis for Li-ion batteries","volume":"6","author":"yan","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/I2MTC.2013.6555479"},{"key":"ref8","first-page":"1","article-title":"Uncertainty quantification in fatigue crack growth prognosis","volume":"2","author":"sankararaman","year":"2011","journal-title":"Int Conf Prognostics Health Manage"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2012.2222650"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2012.2224079"},{"key":"ref9","first-page":"50","article-title":"Uncertainty assessment of prognostics of electronics subject to random vibration","author":"gu","year":"0","journal-title":"Proc AAAI Fall Symp Artif Intell Prognostics"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2014.2336599"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/3468.823474"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2014.7040070"},{"key":"ref21","first-page":"1","article-title":"Fault diagnosis and prognosis based on Lebesgue sampling","volume":"5","author":"zhang","year":"0","journal-title":"Proc Annu Conf Prognostics Health Manage Soc"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1109\/TASE.2012.2230628","article-title":"Decentralized fault diagnosis of continuous annealing processes based on multilevel PCA","volume":"10","author":"liu","year":"2013","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2015.2494529"},{"key":"ref26","first-page":"1","article-title":"A survey of artificial intelligence for prognostics","author":"schwabacher","year":"0","journal-title":"Proc AAAI Fall Symp Artif Intell Prognostics"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2014.2311760"}],"container-title":["IEEE Transactions on Industrial Electronics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/41\/8031005\/07920297.pdf?arnumber=7920297","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T17:03:25Z","timestamp":1642007005000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7920297\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10]]},"references-count":30,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tie.2017.2701790","relation":{},"ISSN":["0278-0046","1557-9948"],"issn-type":[{"value":"0278-0046","type":"print"},{"value":"1557-9948","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,10]]}}}