{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:32:18Z","timestamp":1786116738292,"version":"build-2736575974"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100005230","name":"Chongqing Natural Science Foundation","doi-asserted-by":"publisher","award":["CSTB2025NSCQ-LZX0016"],"award-info":[{"award-number":["CSTB2025NSCQ-LZX0016"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004867","name":"Chongqing University of Technology","doi-asserted-by":"publisher","award":["2025ZDZ031"],"award-info":[{"award-number":["2025ZDZ031"]}],"id":[{"id":"10.13039\/501100004867","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003787","name":"Hebei Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["E2025203237"],"award-info":[{"award-number":["E2025203237"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Reliability Engineering & System Safety"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.ress.2026.113150","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T20:16:54Z","timestamp":1783628214000},"page":"113150","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P3","title":["Distribution parameter alignment-based health indicator for degradation assessment and incipient fault detection of wind turbine bearings under non-stationary operating conditions"],"prefix":"10.1016","volume":"277","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7338-2407","authenticated-orcid":false,"given":"Dingliang","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ress.2026.113150_bib0001","article-title":"A polynomial speed normalized health indicator for both incipient fault detection and prognosis of variable-speed wind turbine bearings","volume":"66","author":"Chen","year":"2025","journal-title":"Adv Eng Info"},{"key":"10.1016\/j.ress.2026.113150_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.110995","article-title":"Reconstruction-based deep unsupervised adaptive threshold support vector data description for wind turbine anomaly detection","volume":"260","author":"Peng","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113150_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110554","article-title":"Wind turbine fault detection and identification via self-attention-based dynamic graph representation learning and variable-level normalizing flow","volume":"253","author":"Zhu","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113150_bib0004","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.2026.113150_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.110823","article-title":"Benchmark transformation neural network for health indicator construction under time-varying speed and its application in machinery prognostics","volume":"257","author":"Yang","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113150_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109356","article-title":"A systematic overview of health indicator construction methods for rotating machinery","volume":"138","author":"Zhou","year":"2024","journal-title":"Eng Appl Artif Intell"},{"key":"10.1016\/j.ress.2026.113150_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110746","article-title":"Fault diagnosis of wind turbine structures with a triaxial vibration dual-branch feature fusion network","volume":"256","author":"Guan","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113150_bib0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.110998","article-title":"A novel sparrow search algorithm based co-correlation graph construction strategy for wind turbine group anomaly identification via graph attention networks","volume":"260","author":"Wang","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113150_bib0009","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.renene.2016.01.006","article-title":"Analysing RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes","volume":"91","author":"Igba","year":"2016","journal-title":"Renew Energy"},{"issue":"2","key":"10.1016\/j.ress.2026.113150_bib0010","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.ymssp.2004.09.002","article-title":"The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines","volume":"20","author":"Antoni","year":"2006","journal-title":"Mech Syst Signal Process"},{"issue":"6","key":"10.1016\/j.ress.2026.113150_bib0011","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1006\/mssp.1997.0115","article-title":"Crest factor and kurtosis contributions to identify defects inducing periodical impulsive forces","volume":"11","author":"Pachaud","year":"1997","journal-title":"Mech Syst Signal Process"},{"issue":"12","key":"10.1016\/j.ress.2026.113150_bib0012","doi-asserted-by":"crossref","first-page":"7762","DOI":"10.1109\/TIE.2015.2455055","article-title":"An improved exponential model for predicting remaining useful life of rolling element bearings","volume":"62","author":"Li","year":"2015","journal-title":"IEEE Trans Indus Electr"},{"key":"10.1016\/j.ress.2026.113150_bib0013","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.ymssp.2016.10.027","article-title":"Remaining useful life prediction for an adaptive skew-wiener process model","volume":"87","author":"Huang","year":"2017","journal-title":"Mech Syst Signal Process"},{"issue":"12","key":"10.1016\/j.ress.2026.113150_bib0014","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/aa8a57","article-title":"Improvement of kurtosis-guided-grams via Gini index for bearing fault feature identification","volume":"28","author":"Miao","year":"2017","journal-title":"Meas Sci Technol"},{"key":"10.1016\/j.ress.2026.113150_bib0015","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.ymssp.2015.04.034","article-title":"The infogram: entropic evidence of the signature of repetitive transients","volume":"74","author":"Antoni","year":"2016","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.113150_bib0016","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.ymssp.2017.11.013","article-title":"Spectral L2\/L1 norm: a new perspective for spectral kurtosis for characterizing non-stationary signals","volume":"104","author":"Wang","year":"2018","journal-title":"Mech Syst Signal Process"},{"issue":"9","key":"10.1016\/j.ress.2026.113150_bib0017","article-title":"Non-negative matrix factorization with sparseness constraints","volume":"5","author":"Hoyer","year":"2004","journal-title":"J Machine Learn Res"},{"issue":"4","key":"10.1016\/j.ress.2026.113150_bib0018","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1109\/TR.2015.2419220","article-title":"An age- and state-dependent nonlinear prognostic model for degrading systems","volume":"64","author":"Zhang","year":"2015","journal-title":"IEEE Trans Reliab"},{"key":"10.1016\/j.ress.2026.113150_bib0019","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.ymssp.2016.01.018","article-title":"Development of a gear vibration indicator and its application in gear wear monitoring","volume":"76-77","author":"Hu","year":"2016","journal-title":"Mech Syst Signal Process"},{"issue":"3","key":"10.1016\/j.ress.2026.113150_bib0020","doi-asserted-by":"crossref","first-page":"2592","DOI":"10.1016\/j.eswa.2010.08.049","article-title":"Application of relevance vector machine and survival probability to machine degradation assessment","volume":"38","author":"Widodo","year":"2011","journal-title":"Expert Syst Appl"},{"key":"10.1016\/j.ress.2026.113150_bib0021","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.mechmachtheory.2019.03.036","article-title":"Performance degradation assessment of a wind turbine gearbox based on multi-sensor data fusion","volume":"137","author":"Pan","year":"2019","journal-title":"Mech Mach Theory"},{"issue":"12","key":"10.1016\/j.ress.2026.113150_bib0022","doi-asserted-by":"crossref","first-page":"10865","DOI":"10.1109\/TIE.2019.2959492","article-title":"Macroscopic\u2013microscopic attention in LSTM networks based on fusion features for gear remaining life prediction","volume":"67","author":"Qin","year":"2020","journal-title":"IEEE Trans Indus Electr"},{"key":"10.1016\/j.ress.2026.113150_bib0023","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.neucom.2018.02.083","article-title":"Machinery health indicator construction based on convolutional neural networks considering trend burr","volume":"292","author":"Guo","year":"2018","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ress.2026.113150_bib0024","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jmsy.2019.11.008","article-title":"Health indicator construction of machinery based on end-to-end trainable convolution recurrent neural networks","volume":"54","author":"Chen","year":"2020","journal-title":"J Manuf Syst"},{"key":"10.1016\/j.ress.2026.113150_bib0025","article-title":"A convolutional neural network based degradation indicator construction and health prognosis using bidirectional long short-term memory network for rolling bearings","volume":"48","author":"Cheng","year":"2021","journal-title":"Adv Eng Info"},{"key":"10.1016\/j.ress.2026.113150_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109399","article-title":"Health indicator construction for degradation assessment by embedded LSTM\u2013CNN autoencoder and growing self-organized map","volume":"252","author":"Chen","year":"2022","journal-title":"Knowl Based Syst"},{"key":"10.1016\/j.ress.2026.113150_bib0027","doi-asserted-by":"crossref","DOI":"10.1109\/TIM.2023.3251399","article-title":"A new deep tensor autoencoder network for unsupervised health indicator construction and degradation state evaluation of metro wheel","volume":"72","author":"Mao","year":"2023","journal-title":"IEEE Trans Instrum Meas"},{"issue":"11","key":"10.1016\/j.ress.2026.113150_bib0028","doi-asserted-by":"crossref","first-page":"5022","DOI":"10.1109\/TNNLS.2020.3026644","article-title":"A shape-constrained neural data fusion network for health index construction and residual life prediction","volume":"32","author":"Li","year":"2020","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10.1016\/j.ress.2026.113150_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2025.111706","article-title":"A hybrid data-driven prognostic scheme based on unsupervised health indicator construction and random-effects Wiener process","volume":"212","author":"Guo","year":"2026","journal-title":"Comput Ind Eng"},{"issue":"4","key":"10.1016\/j.ress.2026.113150_bib0030","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.21595\/jve.2014.15016","article-title":"Health assessment of rotary machinery based on integrated feature selection and Gaussian mixed model","volume":"16","author":"Wang","year":"2014","journal-title":"J Vibroeng"},{"key":"10.1016\/j.ress.2026.113150_bib0031","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.asoc.2018.06.038","article-title":"An integrated approach to bearing prognostics based on EEMD-multi feature extraction, gaussian mixture models and Jensen-R\u00e9nyi divergence","volume":"71","author":"Rai","year":"2018","journal-title":"Appl Soft Comput"},{"key":"10.1016\/j.ress.2026.113150_bib0032","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107562","article-title":"A new unsupervised health index estimation method for bearings early fault detection based on gaussian mixture model","volume":"128","author":"Wen","year":"2024","journal-title":"Eng Appl Artif Intell"},{"key":"10.1016\/j.ress.2026.113150_bib0033","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"},{"key":"10.1016\/j.ress.2026.113150_bib0034","first-page":"1","article-title":"Remaining useful life prediction of bearings based on adaptive degradation point detection and a hybrid deep learning framework","volume":"50","author":"Li","year":"2026","journal-title":"Trans Canadian Soc Mech Eng"},{"key":"10.1016\/j.ress.2026.113150_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107058","article-title":"Construction of bearing health indicator under time-varying operating conditions based on Isolation Forest","volume":"126","author":"Sim","year":"2023","journal-title":"Eng Appl Artif Intell"},{"key":"10.1016\/j.ress.2026.113150_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110139","article-title":"Conditional feature disentanglement learning for anomaly detection in machines operating under time-varying conditions","volume":"191","author":"Zhou","year":"2023","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.113150_bib0037","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110109","article-title":"A speed normalized autoencoder for rotating machinery fault detection under varying speed conditions","volume":"189","author":"Rao","year":"2023","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.113150_bib0038","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.2026.113150_bib0039","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.112464","article-title":"Statistical distribution measures based on amplitude normalization for wind turbine generator bearing condition monitoring under variable speed conditions","volume":"228","author":"Zhang","year":"2025","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.113150_bib0040","article-title":"A dual-decoupling network for anomaly detection in automotive transmissions under time-varying operating conditions","volume":"69","author":"Sun","year":"2026","journal-title":"Adv Eng Info"},{"issue":"6","key":"10.1016\/j.ress.2026.113150_bib0041","doi-asserted-by":"crossref","DOI":"10.1115\/1.4028321","article-title":"Performance degradation assessment for bearing based on ensemble empirical mode decomposition and gaussian mixture model","volume":"136","author":"Hong","year":"2014","journal-title":"J Vib Acoust"},{"key":"10.1016\/j.ress.2026.113150_bib0042","article-title":"Remaining useful life prediction of bearings by a new reinforced memory GRU network","volume":"53","author":"Zhou","year":"2022","journal-title":"Adv Eng Info"},{"key":"10.1016\/j.ress.2026.113150_bib0043","article-title":"Sigmoid informed fuzzy entropy as a health indicator for continuous health monitoring of rolling bearing","volume":"69","author":"Wang","year":"2026","journal-title":"Adv Eng Info"},{"key":"10.1016\/j.ress.2026.113150_bib0044","series-title":"Advances in Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the 6th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations","article-title":"Convolutional neural networks for fault diagnosis using rotating speed normalized vibration","author":"Wei","year":"2019"}],"container-title":["Reliability Engineering & System Safety"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026009592?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026009592?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T11:45:31Z","timestamp":1786016731000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0951832026009592"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":44,"alternative-id":["S0951832026009592"],"URL":"https:\/\/doi.org\/10.1016\/j.ress.2026.113150","relation":{},"ISSN":["0951-8320"],"issn-type":[{"value":"0951-8320","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Distribution parameter alignment-based health indicator for degradation assessment and incipient fault detection of wind turbine bearings under non-stationary operating conditions","name":"articletitle","label":"Article Title"},{"value":"Reliability Engineering & System Safety","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ress.2026.113150","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":"113150"}}