{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T23:04:06Z","timestamp":1773961446960,"version":"3.50.1"},"reference-count":54,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012659","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72271200"],"award-info":[{"award-number":["72271200"]}],"id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012659","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72231008"],"award-info":[{"award-number":["72231008"]}],"id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012659","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72501282"],"award-info":[{"award-number":["72501282"]}],"id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017596","name":"Natural Science Basic Research Program of Shaanxi Province","doi-asserted-by":"publisher","award":["2025JC-QYXQ-043"],"award-info":[{"award-number":["2025JC-QYXQ-043"]}],"id":[{"id":"10.13039\/501100017596","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University","award":["CX2025012"],"award-info":[{"award-number":["CX2025012"]}]},{"name":"Science and Technology Innovation Group Program of Shaanxi Province","award":["2024RS-CXTD-28"],"award-info":[{"award-number":["2024RS-CXTD-28"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Rel."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tr.2026.3667980","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T21:00:10Z","timestamp":1772830810000},"page":"1239-1254","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid Bayesian Learning Framework for Uncertainty-Aware Continuous RUL Prediction With Diffusion-Based Generative Replay"],"prefix":"10.1109","volume":"75","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6090-3699","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Northwestern Polytechnical University, Xi&#x2019;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7108-637X","authenticated-orcid":false,"given":"Enrico","family":"Zio","sequence":"additional","affiliation":[{"name":"Energy Department, Politecnico di Milano, Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7452-2192","authenticated-orcid":false,"given":"Yuantao","family":"Yao","sequence":"additional","affiliation":[{"name":"Energy Department, Politecnico di Milano, Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7380-8110","authenticated-orcid":false,"given":"Zhiqiang","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Northwestern Polytechnical University, Xi&#x2019;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2297-4423","authenticated-orcid":false,"given":"Shubin","family":"Si","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Northwestern Polytechnical University, Xi&#x2019;an, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3183123"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.11.016"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2024.142879"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3138510"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2023.3326487"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2024.110394"},{"key":"ref7","first-page":"1","article-title":"Prognostics and remaining useful life prediction of machinery: Advances, opportunities and challenges","volume":"2","author":"Gebraeel","year":"2023","journal-title":"J. Dyn., Monit. Diagn."},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2022.3202606"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2022.111782"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3462723"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2024.3519347"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3201977"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2023.3271661"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2025.111049"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2023.3294939"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2024.110549"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2025.103577"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.113721"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2024.3450077"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2023.3308092"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2025.111177"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.108119"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2024.3427797"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3210933"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2025.3577829"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109815"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2023.11.009"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2025.3551011"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2024.3522678"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2019.2907440"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2020.3009593"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2023.109421"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121859"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.114214"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2025.103515"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.5555\/2986459.2986721"},{"key":"ref37","first-page":"1727","article-title":"Streaming variational Bayes","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"26","author":"Broderick","year":"2013"},{"key":"ref38","article-title":"Variational continual learning","author":"Nguyen","year":"2017"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295309"},{"key":"ref40","first-page":"1184","article-title":"Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Depeweg","year":"2018"},{"key":"ref41","article-title":"Auto-encoding variational Bayes","author":"Kingma","year":"2013"},{"key":"ref42","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ho","year":"2020"},{"key":"ref43","article-title":"Conditional image generation with score-based diffusion models","author":"Batzolis","year":"2021"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/130385.130417"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2018.2882682"},{"key":"ref46","first-page":"1","article-title":"PRONOSTIA: An experimental platform for bearings accelerated degradation tests","volume-title":"Proc. IEEE Int. Conf. Prognostics Health Manage.","author":"Nectoux","year":"2012"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2023.110688"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2014.2336616"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TASSP.1981.1163711"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2011.2162110"},{"key":"ref51","first-page":"2575","article-title":"Variational dropout and the local reparameterization trick","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Kingma","year":"2015"},{"key":"ref52","article-title":"Uncertainty estimations by softplus normalization in Bayesian convolutional neural networks with variational inference","author":"Shridhar","year":"2018"},{"key":"ref53","first-page":"2498","article-title":"Variational dropout sparsifies deep neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Molchanov","year":"2017"},{"key":"ref54","article-title":"How to train deep variational autoencoders and probabilistic ladder networks","author":"Snderby","year":"2016"}],"container-title":["IEEE Transactions on Reliability"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/24\/11317936\/11422346.pdf?arnumber=11422346","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T20:06:44Z","timestamp":1773950804000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11422346\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":54,"URL":"https:\/\/doi.org\/10.1109\/tr.2026.3667980","relation":{},"ISSN":["0018-9529","1558-1721"],"issn-type":[{"value":"0018-9529","type":"print"},{"value":"1558-1721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}