{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T21:59:16Z","timestamp":1780955956718,"version":"3.54.1"},"reference-count":63,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","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","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Reliability Engineering &amp; System Safety"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.ress.2026.112803","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:10:15Z","timestamp":1777569015000},"page":"112803","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P2","title":["Evidential semi-supervised domain generalization for machine remaining useful life prediction with right-censored data"],"prefix":"10.1016","volume":"275","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1693-0910","authenticated-orcid":false,"given":"Jie","family":"Shang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1749-7223","authenticated-orcid":false,"given":"Chao","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1485-0722","authenticated-orcid":false,"given":"Liang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haobo","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7108-637X","authenticated-orcid":false,"given":"Enrico","family":"Zio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ress.2026.112803_bib0001","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1016\/j.ymssp.2017.11.016","article-title":"Machinery health prognostics: a systematic review from data acquisition to RUL prediction","volume":"104","author":"Lei","year":"2018","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112803_bib0002","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":"10.1016\/j.ress.2026.112803_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2021.108119","article-title":"Prognostics and health management (PHM): where are we and where do we (need to) go in theory and practice","volume":"218","author":"Zio","year":"2022","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112803_bib0004","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.jmsy.2024.02.011","article-title":"A novel data augmentation framework for remaining useful life estimation with dense convolutional regression network","volume":"74","author":"Shang","year":"2024","journal-title":"J Manuf Syst"},{"key":"10.1016\/j.ress.2026.112803_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107389","article-title":"Dynamic time scales ensemble framework for similarity-based remaining useful life prediction under multiple failure modes","volume":"127","author":"Xu","year":"2024","journal-title":"Eng Appl Artif Intell"},{"key":"10.1016\/j.ress.2026.112803_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2022.109610","article-title":"Time-varying trajectory modeling via dynamic governing network for remaining useful life prediction","volume":"182","author":"Zhou","year":"2023","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112803_bib0007","doi-asserted-by":"crossref","first-page":"4332","DOI":"10.1038\/s41467-024-48779-z","article-title":"Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis","volume":"15","author":"Wang","year":"2024","journal-title":"Nat Commun"},{"key":"10.1016\/j.ress.2026.112803_bib0008","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.jmsy.2023.04.002","article-title":"A piecewise method for bearing remaining useful life estimation using temporal convolutional networks","volume":"68","author":"Qiu","year":"2023","journal-title":"J Manuf Syst"},{"key":"10.1016\/j.ress.2026.112803_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110721","article-title":"Remaining useful life prediction of lithium-ion battery with nonparametric degradation modeling and incomplete data","volume":"256","author":"Li","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112803_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.112449","article-title":"A survey on graph neural networks for remaining useful life prediction: methodologies, evaluation and future trends","volume":"229","author":"Wang","year":"2025","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112803_bib0011","doi-asserted-by":"crossref","first-page":"3208","DOI":"10.1109\/TIE.2018.2844856","article-title":"Estimation of bearing remaining useful life based on multiscale convolutional neural network","volume":"66","author":"Zhu","year":"2019","journal-title":"IEEE Trans Ind Electron"},{"key":"10.1016\/j.ress.2026.112803_bib0012","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110252","article-title":"Single domain generalization method based on anti-causal learning for rotating machinery fault diagnosis","volume":"250","author":"Zhang","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112803_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111057","article-title":"DG-Softmax: a new domain generalization intelligent fault diagnosis method for planetary gearboxes","volume":"260","author":"Qian","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112803_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.112965","article-title":"Out-of-domain generalization for remaining useful life prediction of rotating machinery from a single source: an adversarial contrastive learning approach","volume":"236","author":"Shang","year":"2025","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112803_bib0015","doi-asserted-by":"crossref","first-page":"10781","DOI":"10.1109\/TPAMI.2024.3444904","article-title":"SEA++: multi-graph-based higher-order sensor alignment for multivariate time-series unsupervised domain adaptation","volume":"46","author":"Wang","year":"2024","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10.1016\/j.ress.2026.112803_bib0016","doi-asserted-by":"crossref","first-page":"4143","DOI":"10.1109\/TMECH.2022.3147534","article-title":"Transfer learning for remaining useful life prediction across operating conditions based on multisource domain adaptation","volume":"27","author":"Ding","year":"2022","journal-title":"IEEE\/ASME Trans Mechatron"},{"key":"10.1016\/j.ress.2026.112803_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109695","article-title":"Tensor representation-based transferability analytics and selective transfer learning of prognostic knowledge for remaining useful life prediction across machines","volume":"242","author":"Mao","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112803_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110239","article-title":"Transfer learning algorithms for bearing remaining useful life prediction: a comprehensive review from an industrial application perspective","volume":"193","author":"Chen","year":"2023","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112803_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110769","article-title":"Remaining useful life prediction of rotating equipment under multiple operating conditions via multi-source adversarial distillation domain adaptation","volume":"256","author":"Shang","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112803_bib0020","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1109\/TII.2024.3463705","article-title":"Multi-source domain generalization for machine remaining useful life prediction via risk minimization-based test-time adaptation","volume":"21","author":"Zhang","year":"2025","journal-title":"IEEE Trans Ind Inf"},{"key":"10.1016\/j.ress.2026.112803_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112915","article-title":"A generalized network with domain invariance and specificity representation for bearing remaining useful life prediction under unknown conditions","volume":"310","author":"Zheng","year":"2025","journal-title":"Knowl Based Syst"},{"key":"10.1016\/j.ress.2026.112803_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.113079","article-title":"Common distribution discrepancy knowledge distilling: a new out-of-distribution generalization framework for machinery RUL prediction","volume":"237","author":"Qian","year":"2025","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112803_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.111924","article-title":"Domain generalization for rotating machinery real-time remaining useful life prediction via multi-domain orthogonal degradation feature exploration","volume":"223","author":"Shang","year":"2025","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112803_bib0024","doi-asserted-by":"crossref","first-page":"4675","DOI":"10.1109\/TMECH.2022.3163289","article-title":"Health assessment of rotating equipment with unseen conditions using adversarial domain generalization toward self-supervised regularization learning","volume":"27","author":"Zhuang","year":"2022","journal-title":"IEEE\/ASME Trans Mechatron"},{"key":"10.1016\/j.ress.2026.112803_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.110199","article-title":"Domain generalization via adversarial out-domain augmentation for remaining useful life prediction of bearings under unseen conditions","volume":"261","author":"Ding","year":"2023","journal-title":"Knowl Based Syst"},{"key":"10.1016\/j.ress.2026.112803_bib0026","doi-asserted-by":"crossref","first-page":"6177","DOI":"10.1109\/TII.2023.3342885","article-title":"An optimal-subdomain generalization method for remaining useful life prediction of machinery under time-varying operation conditions","volume":"20","author":"Liu","year":"2024","journal-title":"IEEE Trans Ind Inf"},{"key":"10.1016\/j.ress.2026.112803_bib0027","unstructured":"C. Lillelund, F. Pannullo, M. Jakobsen, M. Morante, C. Pedersen, RULSurv: a probabilistic survival-based method for early censoring-aware prediction of remaining useful life in ball bearings, 2025, 10.48550\/arXiv.2405.01614."},{"key":"10.1016\/j.ress.2026.112803_bib0028","doi-asserted-by":"crossref","first-page":"5704","DOI":"10.1109\/TSMC.2024.3408058","article-title":"Heterogeneous federated domain generalization network with common representation learning for cross-load machinery fault diagnosis","volume":"54","author":"Qian","year":"2024","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"10.1016\/j.ress.2026.112803_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2023.121355","article-title":"T-shape data and probabilistic remaining useful life prediction for Li-ion batteries using multiple non-crossing quantile long short-term memory","volume":"349","author":"Ly","year":"2023","journal-title":"Appl Energy"},{"key":"10.1016\/j.ress.2026.112803_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108921","article-title":"Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging","volume":"230","author":"Li","year":"2023","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112803_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110891","article-title":"Attention-aware temporal\u2013spatial graph neural network with multi-sensor information fusion for fault diagnosis","volume":"278","author":"Wang","year":"2023","journal-title":"Knowl Based Syst"},{"key":"10.1016\/j.ress.2026.112803_bib0032","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2022.110074","article-title":"Mutual-assistance semisupervised domain generalization network for intelligent fault diagnosis under unseen working conditions","volume":"189","author":"Zhao","year":"2023","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112803_bib0033","doi-asserted-by":"crossref","first-page":"16744","DOI":"10.1007\/s11227-024-06111-y","article-title":"M2BIST-SPNet: RUL prediction for railway signaling electromechanical devices","volume":"80","author":"Hu","year":"2024","journal-title":"J Supercomput"},{"key":"10.1016\/j.ress.2026.112803_bib0034","doi-asserted-by":"crossref","first-page":"5938","DOI":"10.1109\/TTE.2024.3493939","article-title":"Improving battery life prediction with unlabeled data: confidence-weighted semi-supervised learning with label propagation","volume":"11","author":"Zhang","year":"2025","journal-title":"IEEE Trans Transp Electrific"},{"key":"10.1016\/j.ress.2026.112803_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.108657","article-title":"A semi-supervised GAN method for RUL prediction using failure and suspension histories","volume":"168","author":"He","year":"2022","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112803_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.119824","article-title":"Semi-supervised machinery health assessment framework via temporal broad learning system embedding manifold regularization with unlabeled data","volume":"222","author":"Cao","year":"2023","journal-title":"Expert Syst Appl"},{"key":"10.1016\/j.ress.2026.112803_bib0037","doi-asserted-by":"crossref","first-page":"1820","DOI":"10.1016\/j.joule.2024.02.020","article-title":"Semi-supervised learning for explainable few-shot battery lifetime prediction","volume":"8","author":"Guo","year":"2024","journal-title":"Joule"},{"key":"10.1016\/j.ress.2026.112803_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2025.117152","article-title":"Privacy-preserving federated semi-supervised learning for battery life prediction amid data scarcity","volume":"128","author":"Ma","year":"2025","journal-title":"J Energy Storage"},{"key":"10.1016\/j.ress.2026.112803_bib0039","first-page":"1","article-title":"Evidential domain adaptation for remaining useful life prediction with incomplete degradation","volume":"74","author":"Hou","year":"2025","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.ress.2026.112803_bib0040","doi-asserted-by":"crossref","first-page":"1903","DOI":"10.1109\/TMECH.2023.3325538","article-title":"Partial domain adaptation in remaining useful life prediction with incomplete target data","volume":"29","author":"Li","year":"2023","journal-title":"IEEE\/ASME Trans Mechatron"},{"key":"10.1016\/j.ress.2026.112803_bib0041","first-page":"1","article-title":"A novel portable edge-intelligent system for cross-individual fault diagnosis","volume":"75","author":"Luo","year":"2026","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.ress.2026.112803_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2025.104359","article-title":"An individual generalization framework based on independent samples towards a more reasonable fault diagnosis benchmark","volume":"173","author":"He","year":"2025","journal-title":"Comput Ind"},{"key":"10.1016\/j.ress.2026.112803_bib0043","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TSMC.2026.3663567","article-title":"CEC-FedISDG: a cloud-edge collaboration federated invariance and specificity domain generalization method for machine remaining useful life prediction","author":"Shang","year":"2026","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"10.1016\/j.ress.2026.112803_bib0044","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.109964","article-title":"Domain generalization for cross-domain fault diagnosis: an application-oriented perspective and a benchmark study","volume":"245","author":"Zhao","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112803_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.103063","article-title":"Domain generalization for rotating machinery fault diagnosis: a survey","volume":"64","author":"Xiao","year":"2025","journal-title":"Adv Eng Inform"},{"key":"10.1016\/j.ress.2026.112803_bib0046","first-page":"1","article-title":"Balancing discrepancy and consistency: adversarial single domain generalization in fault diagnosis","author":"Zhang","year":"2025","journal-title":"IEEE Trans Ind Inf"},{"key":"10.1016\/j.ress.2026.112803_bib0047","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.113527","article-title":"CITDG: a causality and information-theory inspired domain generalization method for machine remaining useful life prediction in unseen domains","volume":"241","author":"Shang","year":"2025","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112803_bib0048","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111431","article-title":"Remaining useful life prediction considering multiple uncertainty information via Bayesian BiGRU-based method","volume":"264","author":"Chen","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112803_bib0049","series-title":"Proceedings of the 2021 IEEE international conference on prognostics and health management (ICPHM)","first-page":"1","article-title":"A hybrid Bayesian deep learning model for remaining useful life prognostics and uncertainty quantification","author":"Huang","year":"2021"},{"key":"10.1016\/j.ress.2026.112803_bib0050","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.jpowsour.2017.05.004","article-title":"Gaussian process regression for forecasting battery state of health","volume":"357","author":"Richardson","year":"2017","journal-title":"J Power Sources"},{"key":"10.1016\/j.ress.2026.112803_bib0051","series-title":"Proceedings of the IECON 2019-45th annual conference of the IEEE industrial electronics society","first-page":"6004","article-title":"Gaussian process regression remaining useful lifetime prediction of thermally aged power IGBT","author":"Ismail","year":"2019"},{"key":"10.1016\/j.ress.2026.112803_bib0052","first-page":"14927","article-title":"Deep evidential regression","volume":"33","author":"Amini","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"10.1016\/j.ress.2026.112803_bib0053","doi-asserted-by":"crossref","first-page":"37","DOI":"10.2478\/jaiscr-2025-0003","article-title":"Remaining useful life prediction with uncertainty quantification using evidential deep learning","volume":"15","author":"Ayed","year":"2025","journal-title":"J Artif Intell Soft Comput Res"},{"key":"10.1016\/j.ress.2026.112803_bib0054","unstructured":"F. H\u00fcttel, F. Rodrigues, F. Pereira, Deep evidential learning for bayesian quantile regression, 2023, 10.48550\/arXiv.2308.10650."},{"key":"10.1016\/j.ress.2026.112803_bib0055","unstructured":"A. Tarvainen, H. Valpola, Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results, 2018, 10.48550\/arXiv.1703.01780."},{"key":"10.1016\/j.ress.2026.112803_bib0056","doi-asserted-by":"crossref","first-page":"3459","DOI":"10.1007\/s10845-023-02215-z","article-title":"Towards prognostic generalization: a domain conditional invariance and specificity disentanglement network for remaining useful life prediction","volume":"35","author":"Xia","year":"2024","journal-title":"J Intell Manuf"},{"key":"10.1016\/j.ress.2026.112803_bib0057","first-page":"1","article-title":"A life-stage domain aware network for bearing health prognosis under unseen temporal distribution shift","volume":"73","author":"Hu","year":"2024","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.ress.2026.112803_bib0058","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108986","article-title":"A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition","volume":"231","author":"Zhang","year":"2023","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112803_bib0059","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.129938","article-title":"Unseen class feature regeneration adversarial learning method for time-varying cross-domain fault diagnosis","volume":"298","author":"Wang","year":"2026","journal-title":"Expert Syst Appl"},{"key":"10.1016\/j.ress.2026.112803_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2022.110074","article-title":"Mutual-assistance semisupervised domain generalization network for intelligent fault diagnosis under unseen working conditions","volume":"189","author":"Zhao","year":"2023","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112803_bib0061","series-title":"Proceedings of the 2012 IEEE international conference on prognostics and health management","first-page":"1","article-title":"PRONOSTIA, an experimental platform for bearings accelerated degradation tests","author":"Nectoux","year":"2012"},{"key":"10.1016\/j.ress.2026.112803_bib0062","series-title":"Proceedings of the PHM society European conference","first-page":"721","article-title":"Remaining useful lifetime estimation of bearings operating under time-varying conditions","author":"Javanmardi","year":"2024"},{"key":"10.1016\/j.ress.2026.112803_bib0063","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113364","article-title":"Semi-supervised domain generalization with clustering and contrastive learning combined mechanism","volume":"318","author":"Ying","year":"2025","journal-title":"Knowl Based Syst"}],"container-title":["Reliability Engineering &amp; System Safety"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026006149?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026006149?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T21:19:08Z","timestamp":1780953548000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0951832026006149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":63,"alternative-id":["S0951832026006149"],"URL":"https:\/\/doi.org\/10.1016\/j.ress.2026.112803","relation":{},"ISSN":["0951-8320"],"issn-type":[{"value":"0951-8320","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Evidential semi-supervised domain generalization for machine remaining useful life prediction with right-censored data","name":"articletitle","label":"Article Title"},{"value":"Reliability Engineering & System Safety","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ress.2026.112803","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"112803"}}