{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T22:04:37Z","timestamp":1780351477660,"version":"3.54.1"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"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"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Reliability Engineering &amp; System Safety"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.ress.2026.112909","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T15:39:06Z","timestamp":1779464346000},"page":"112909","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A semi-observable state-driven self-tuning remaining useful life prediction method for mechanical systems"],"prefix":"10.1016","volume":"276","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0678-8161","authenticated-orcid":false,"given":"Naipeng","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nanzhi","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaguo","family":"Lei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3015-3580","authenticated-orcid":false,"given":"Bin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0569-2176","authenticated-orcid":false,"given":"Xiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4282-3262","authenticated-orcid":false,"given":"Xiaosheng","family":"Si","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ress.2026.112909_bib0001","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.108315","article-title":"A self-data-driven method for remaining useful life prediction of wind turbines considering continuously varying speeds","volume":"165","author":"Li","year":"2022","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112909_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110224","article-title":"A nonparametric degradation modeling method for remaining useful life prediction with fragment data","volume":"249","author":"Li","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112909_bib0003","article-title":"An integrated approach of knowledge-driven and neural network for fatigue remaining useful life prediction within small sample conditions","author":"Fan","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"issue":"1","key":"10.1016\/j.ress.2026.112909_bib0004","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/TR.2019.2895501","article-title":"Reliability assessment of a hierarchical system subjected to inconsistent priors and multilevel data","volume":"69","author":"Yang","year":"2019","journal-title":"IEEE Trans Reliab"},{"key":"10.1016\/j.ress.2026.112909_bib0005","article-title":"Semi-supervised cross-domain fault diagnosis via contrastive pre-training and annotation-efficient alignment strategy","volume":"50","author":"Yang","year":"2026","journal-title":"J Ind Inf Integr"},{"issue":"1","key":"10.1016\/j.ress.2026.112909_bib0006","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1109\/TR.2023.3294507","article-title":"Reliability evaluation of an imprecise multistate system with mixed uncertainty","volume":"73","author":"Yang","year":"2023","journal-title":"IEEE Trans Reliab"},{"issue":"11","key":"10.1016\/j.ress.2026.112909_bib0007","doi-asserted-by":"crossref","first-page":"11482","DOI":"10.1109\/TIE.2020.3038069","article-title":"Multi-sensor data-driven remaining useful life prediction of semi-observable systems","volume":"68","author":"Li","year":"2021","journal-title":"IEEE Trans Ind Electron"},{"key":"10.1016\/j.ress.2026.112909_bib0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2024.114658","article-title":"Multi-sensor data fusion in intelligent fault diagnosis of rotating machines: a comprehensive review","volume":"232","author":"Kibrete","year":"2024","journal-title":"Measurement"},{"key":"10.1016\/j.ress.2026.112909_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2022.108204","article-title":"A novel multi-scale CNN and attention mechanism method with multi-sensor signal for remaining useful life prediction","volume":"169","author":"Xu","year":"2022","journal-title":"Comput Ind Eng"},{"key":"10.1016\/j.ress.2026.112909_bib0010","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.jmsy.2020.11.016","article-title":"A joint classification-regression method for multi-stage remaining useful life prediction","volume":"58","author":"Wu","year":"2021","journal-title":"J Manuf Syst"},{"key":"10.1016\/j.ress.2026.112909_bib0011","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.jmsy.2024.01.008","article-title":"Data-model linkage prediction of tool remaining useful life based on deep feature fusion and wiener process","volume":"73","author":"Li","year":"2024","journal-title":"J Manuf Syst"},{"key":"10.1016\/j.ress.2026.112909_bib0012","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.asoc.2018.03.043","article-title":"Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system","volume":"68","author":"Wu","year":"2018","journal-title":"Appl Soft Comput"},{"key":"10.1016\/j.ress.2026.112909_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2021.107504","article-title":"Remaining useful life prediction and optimal maintenance time determination for a single unit using isotonic regression and gamma process model","volume":"210","author":"Wang","year":"2021","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112909_bib0014","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.ymssp.2016.07.039","article-title":"An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission","volume":"84","author":"Aye","year":"2017","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112909_bib0015","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.ress.2016.11.008","article-title":"Multistream sensor fusion-based prognostics model for systems with single failure modes","volume":"159","author":"Fang","year":"2017","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112909_bib0016","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.isatra.2019.07.004","article-title":"Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network","volume":"97","author":"Wu","year":"2020","journal-title":"ISA Trans"},{"key":"10.1016\/j.ress.2026.112909_bib0017","article-title":"Health index-based remaining useful life prediction using functional principal component analysis with multivariate sensor data","author":"Nian","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112909_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111420","article-title":"Spatial-temporal multi-sensor information fusion network with prior knowledge embedding for equipment remaining useful life prediction","volume":"264","author":"Qin","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"issue":"3","key":"10.1016\/j.ress.2026.112909_bib0019","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.1109\/TR.2018.2831256","article-title":"A hybrid approach to cutting tool remaining useful life prediction based on the Wiener process","volume":"67","author":"Sun","year":"2018","journal-title":"IEEE Trans Reliab"},{"issue":"1\u20132","key":"10.1016\/j.ress.2026.112909_bib0020","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.ymssp.2012.08.016","article-title":"A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation","volume":"35","author":"Si","year":"2013","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112909_bib0021","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.ress.2015.12.016","article-title":"Remaining useful lifetime estimation and noisy gamma deterioration process","volume":"149","author":"Le Son","year":"2016","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112909_bib0022","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.ress.2017.11.017","article-title":"Bayesian and likelihood inferences on remaining useful life in two-phase degradation models under gamma process","volume":"184","author":"Ling","year":"2019","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112909_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109124","article-title":"Feature fusion model based health indicator construction and self-constraint state-space estimator for remaining useful life prediction of bearings in wind turbines","volume":"233","author":"Li","year":"2023","journal-title":"Reliab Eng Syst Saf"},{"issue":"4","key":"10.1016\/j.ress.2026.112909_bib0024","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1109\/TASE.2012.2227960","article-title":"A state-space-based prognostic model for hidden and age-dependent nonlinear degradation process","volume":"10","author":"Feng","year":"2013","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"10.1016\/j.ress.2026.112909_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.109952","article-title":"Box-cox transformation based state-space modeling as a unified prognostic framework for degradation linearization and RUL prediction enhancement","volume":"244","author":"Liu","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"issue":"1","key":"10.1016\/j.ress.2026.112909_bib0026","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/TASE.2019.2921285","article-title":"A double hybrid state-space model for real-time sensor-driven monitoring of deteriorating systems","volume":"17","author":"Skordilis","year":"2019","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"10.1016\/j.ress.2026.112909_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2020.107249","article-title":"Remaining useful life prediction based on a multi-sensor data fusion model","volume":"208","author":"Li","year":"2021","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112909_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110599","article-title":"A hybrid-driven probabilistic state space model for tool wear monitoring","volume":"200","author":"Ma","year":"2023","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112909_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111356","article-title":"Multivariate adaptive brownian motion-particle filter framework for remaining useful life prediction of nonlinear and state-noise coupled degradation process","volume":"264","author":"Bu","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"issue":"6","key":"10.1016\/j.ress.2026.112909_bib0030","doi-asserted-by":"crossref","first-page":"4738","DOI":"10.1109\/TIE.2018.2866057","article-title":"Enhanced particle filtering for bearing remaining useful life prediction of wind turbine drivetrain gearboxes","volume":"66","author":"Cheng","year":"2018","journal-title":"IEEE Trans Ind Electron"},{"issue":"5","key":"10.1016\/j.ress.2026.112909_bib0031","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/MSP.2003.1236770","article-title":"Particle filtering","volume":"20","author":"Djuric","year":"2003","journal-title":"IEEE Signal Proc Mag"},{"issue":"1","key":"10.1016\/j.ress.2026.112909_bib0032","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/02331880309257","article-title":"Bayesian filtering: from Kalman filters to particle filters, and beyond","volume":"37","author":"Chen","year":"2003","journal-title":"Statistics"},{"key":"10.1016\/j.ress.2026.112909_bib0033","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.microrel.2018.08.007","article-title":"A holistic comparison of the different resampling algorithms for particle filter based prognosis using lithium ion batteries as a case study","volume":"91","author":"Pugalenthi","year":"2018","journal-title":"Microelectron Reliab"},{"key":"10.1016\/j.ress.2026.112909_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122958","article-title":"Elastic net-based high dimensional data selection for regression","volume":"244","author":"Chamlal","year":"2024","journal-title":"Expert Syst Appl"},{"key":"10.1016\/j.ress.2026.112909_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2022.109747","article-title":"Research on a remaining useful life prediction method for degradation angle identification two-stage degradation process","volume":"184","author":"Wang","year":"2023","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112909_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110213","article-title":"A new nonparametric degradation modeling method for truncated degradation signals by axis rotation","volume":"192","author":"Li","year":"2023","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112909_bib0037","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110888","article-title":"Multi-scale time series analysis using TT-ConvLSTM technique for bearing remaining useful life prediction","volume":"206","author":"Niazi","year":"2024","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112909_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.113039","article-title":"Remaining useful life prediction by degradation distribution transport health indicator and consolidated memory stabilized LSTM","volume":"236","author":"Zhu","year":"2025","journal-title":"Mech Syst Signal Process"}],"container-title":["Reliability Engineering &amp; System Safety"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026007192?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026007192?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T21:46:54Z","timestamp":1780350414000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0951832026007192"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":38,"alternative-id":["S0951832026007192"],"URL":"https:\/\/doi.org\/10.1016\/j.ress.2026.112909","relation":{},"ISSN":["0951-8320"],"issn-type":[{"value":"0951-8320","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A semi-observable state-driven self-tuning remaining useful life prediction method for mechanical systems","name":"articletitle","label":"Article Title"},{"value":"Reliability Engineering & System Safety","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ress.2026.112909","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":"112909"}}