{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:58:59Z","timestamp":1781225939678,"version":"3.54.1"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"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","award":["52475237"],"award-info":[{"award-number":["52475237"]}],"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","award":["51905549"],"award-info":[{"award-number":["51905549"]}],"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","award":["52130502"],"award-info":[{"award-number":["52130502"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.knosys.2026.115990","type":"journal-article","created":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T21:20:37Z","timestamp":1776028837000},"page":"115990","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Scale-aware weighting adversarial partial domain adaptation with entropy-minimized ambiguity for rolling bearings fault diagnosis"],"prefix":"10.1016","volume":"343","author":[{"given":"Qi","family":"Chang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9122-9059","authenticated-orcid":false,"given":"Congcong","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianghui","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.115990_bib0001","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112606","article-title":"Transfer graph feature alignment guided multi-source domain adaptation network for machinery fault diagnosis","author":"Liu","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115990_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112628","article-title":"Utilizing bayesian generalization network for reliable fault diagnosis of machinery with limited data","author":"Feng","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115990_bib0003","article-title":"A novel interpretable semi-supervised graph learning model for intelligent fault diagnosis of hydraulic pumps","author":"Li","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115990_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112478","article-title":"Multi - timescale attention residual shrinkage network with adaptive global-local denoising for rolling-bearing fault diagnosis","volume":"304","author":"Gao","year":"2024","journal-title":"Knowl. Based Syst."},{"issue":"3","key":"10.1016\/j.knosys.2026.115990_bib0005","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1177\/14759217211009780","article-title":"A new dam structural response estimation paradigm powered by deep learning and transfer learning techniques","volume":"21","author":"Li","year":"2022","journal-title":"Struct. Health Monitor."},{"key":"10.1016\/j.knosys.2026.115990_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112443","article-title":"A source free robust domain adaptation approach with pseudo-labels uncertainty estimation for rolling bearing fault diagnosis under limited sample conditions","volume":"304","author":"Liu","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115990_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112284","article-title":"Cross-domain data fusion generation: a novel composite label-guided generative solution for adaptation diagnosis","volume":"301","author":"Zhang","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115990_bib0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112180","article-title":"Deep conditional adversarial subdomain adaptation network for unsupervised mechanical fault diagnosis","volume":"300","author":"Chen","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115990_bib0009","first-page":"1","article-title":"An adaptive domain adaptation method for rolling bearings\u2019 Fault diagnosis fusing deep convolution and self-attention networks","volume":"72","author":"Yu","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.knosys.2026.115990_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111922","article-title":"Enhancing equipment safeguarding in IIoT: a self-supervised fault diagnosis paradigm based on asymmetric graph autoencoder","volume":"296","author":"Chen","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115990_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110748","article-title":"Maximum mean square discrepancy: a new discrepancy representation metric for mechanical fault transfer diagnosis","volume":"276","author":"Qian","year":"2023","journal-title":"Knowl. Based Syst."},{"issue":"5","key":"10.1016\/j.knosys.2026.115990_bib0012","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ac471d","article-title":"A higher-order moment matching based fine-grained adversarial domain adaptation method for intelligent bearing fault diagnosis","volume":"33","author":"Wang","year":"2022","journal-title":"Measur. Sci. Technol."},{"key":"10.1016\/j.knosys.2026.115990_bib0013","article-title":"A multi-order moment matching-based unsupervised domain adaptation with application to cross-working condition fault diagnosis of rolling bearings","author":"Chang","year":"2024","journal-title":"Struct. Health Monitor."},{"key":"10.1016\/j.knosys.2026.115990_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106236","article-title":"Double-level adversarial domain adaptation network for intelligent fault diagnosis","volume":"205","author":"Jiao","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115990_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.110752","article-title":"A novel deep convolution multi-adversarial domain adaptation model for rolling bearing fault diagnosis","volume":"191","author":"Wan","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.knosys.2026.115990_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122393","article-title":"Novel dual-network autoencoder based adversarial domain adaptation with Wasserstein divergence for fault diagnosis of unlabeled data","volume":"238","author":"Yang","year":"2024","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"10.1016\/j.knosys.2026.115990_bib0017","doi-asserted-by":"crossref","first-page":"2293","DOI":"10.1109\/TMECH.2023.3237233","article-title":"Transformer-enabled cross-domain diagnostics for complex rotating machinery with multiple sensors","volume":"28","author":"Zhang","year":"2023","journal-title":"IEEE\/ASME Transac. Mechatr."},{"key":"10.1016\/j.knosys.2026.115990_bib0018","doi-asserted-by":"crossref","DOI":"10.1109\/JSEN.2024.3479706","article-title":"Cross attention transformer-based domain adaptation: a novel method for fault diagnosis of rotating machinery with high generalizability and alignment capability","author":"Yin","year":"2024","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.knosys.2026.115990_bib0019","article-title":"Universal source-free domain adaptation method for cross-domain fault diagnosis of machines","volume":"191","author":"Zhang","year":"2023","journal-title":"Mech. Syst. Sign. Proc."},{"key":"10.1016\/j.knosys.2026.115990_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109261","article-title":"Dictionary domain adaptation transformer for cross-machine fault diagnosis of rolling bearings","volume":"138","author":"Cui","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.115990_bib0021","article-title":"FEV-Swin: multi-source heterogeneous information fusion under a variant swin transformer framework for intelligent cross-domain fault diagnosis","author":"Zhou","year":"2025","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115990_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.116868","article-title":"Intelligent rubbing fault identification using multivariate signals and a multivariate one-dimensional convolutional neural network","volume":"198","author":"Prosvirin","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.115990_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117297","article-title":"Construction of health indicators for condition monitoring of rotating machinery: a review of the research","volume":"203","author":"Zhou","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.115990_bib0024","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122910","article-title":"Domain adaptation with label-aligned sampling (DALAS) for cross-domain fault diagnosis of rotating machinery under class imbalance","volume":"243","author":"Lee","year":"2024","journal-title":"Expert. Syst. Appl"},{"key":"10.1016\/j.knosys.2026.115990_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.110203","article-title":"A novel conditional weighting transfer Wasserstein auto-encoder for rolling bearing fault diagnosis with multi-source domains","volume":"262","author":"Zhao","year":"2023","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115990_bib0026","article-title":"Moment matching-based intraclass multisource domain adaptation network for bearing fault diagnosis","volume":"168","author":"Xia","year":"2022","journal-title":"Mech. Syst. Sign. Proc."},{"key":"10.1016\/j.knosys.2026.115990_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.110752","article-title":"A novel deep convolution multi-adversarial domain adaptation model for rolling bearing fault diagnosis","volume":"191","author":"Wan","year":"2022","journal-title":"Measurement"},{"issue":"7","key":"10.1016\/j.knosys.2026.115990_bib0028","doi-asserted-by":"crossref","first-page":"8295","DOI":"10.1109\/TII.2022.3217541","article-title":"Unbalanced bearing fault diagnosis under various speeds based on spectrum alignment and deep transfer convolution neural network","volume":"19","author":"Lu","year":"2023","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"5","key":"10.1016\/j.knosys.2026.115990_bib0029","doi-asserted-by":"crossref","first-page":"4351","DOI":"10.1109\/TIE.2020.2984968","article-title":"Deep learning-based partial domain adaptation method on intelligent machinery fault diagnostics","volume":"68","author":"Li","year":"2020","journal-title":"IEEE Transac. Indust. Electr."},{"key":"10.1016\/j.knosys.2026.115990_bib0030","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.neunet.2020.06.014","article-title":"Partial transfer learning in machinery cross-domain fault diagnostics using class-weighted adversarial networks","volume":"129","author":"Li","year":"2020","journal-title":"Neur. Netw."},{"key":"10.1016\/j.knosys.2026.115990_bib0031","first-page":"1","article-title":"Reconstruction domain adaptation transfer network for partial transfer learning of machinery fault diagnostics","volume":"71","author":"Guo","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.knosys.2026.115990_bib0032","first-page":"1","article-title":"Dual-weight consistency-induced partial domain adaptation network for intelligent fault diagnosis of machinery","volume":"71","author":"Kuang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.knosys.2026.115990_bib0033","first-page":"1","article-title":"Partial domain adaptation method based on class-weighted alignment for fault diagnosis of rotating machinery","volume":"71","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.knosys.2026.115990_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110412","article-title":"Intelligent fault diagnosis of partial deep transfer based on multi-representation structural intraclass compact and double-aligned domain adaptation","volume":"197","author":"Li","year":"2023","journal-title":"Mech. Syst. Sign. Process"},{"key":"10.1016\/j.knosys.2026.115990_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.113712","article-title":"Multi-level weighted dynamic adversarial adaptation network for partial set cross-domain fault diagnosis","volume":"223","author":"Zhang","year":"2023","journal-title":"Measurement"},{"issue":"11","key":"10.1016\/j.knosys.2026.115990_bib0036","doi-asserted-by":"crossref","first-page":"6263","DOI":"10.1109\/TNNLS.2021.3073119","article-title":"Adversarial entropy optimization for unsupervised domain adaptation","volume":"33","author":"Ma","year":"2021","journal-title":"IEEE Trans. Neural. Netw. Learn Syst."},{"key":"10.1016\/j.knosys.2026.115990_bib0037","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2020.108901","article-title":"Fault feature extraction and diagnosis of rolling bearings based on wavelet thresholding denoising with CEEMDAN energy entropy and PSO-LSSVM","volume":"172","author":"Chen","year":"2021","journal-title":"Measurement"},{"key":"10.1016\/j.knosys.2026.115990_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.108732","article-title":"Towards better benchmarking using the CWRU bearing fault dataset","volume":"169","author":"Hendriks","year":"2022","journal-title":"Mech Syst Sign. Process"},{"key":"10.1016\/j.knosys.2026.115990_bib0039","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2021.109864","article-title":"Extreme learning machine-based classifier for fault diagnosis of rotating machinery using a residual network and continuous wavelet transform","volume":"183","author":"Wei","year":"2021","journal-title":"Measurement"},{"key":"10.1016\/j.knosys.2026.115990_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.111175","article-title":"Evolvable graph neural network for system-level incremental fault diagnosis of train transmission systems","volume":"210","author":"Ding","year":"2024","journal-title":"Mech. Syst. Sign. Process"},{"key":"10.1016\/j.knosys.2026.115990_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2024.110203","article-title":"Incremental bearing fault diagnosis method under imbalanced sample conditions","volume":"192","author":"Liu","year":"2024","journal-title":"Comp. Indust. Eng."},{"issue":"6","key":"10.1016\/j.knosys.2026.115990_bib0042","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ad3294","article-title":"Robust transfer subspace learning based on low-rank and sparse representation for bearing fault diagnosis","volume":"35","author":"Yu","year":"2024","journal-title":"Measur. Sci. Technol."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126007161?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126007161?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:12:33Z","timestamp":1781223153000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126007161"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":42,"alternative-id":["S0950705126007161"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115990","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Scale-aware weighting adversarial partial domain adaptation with entropy-minimized ambiguity for rolling bearings fault diagnosis","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115990","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115990"}}