{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T18:08:42Z","timestamp":1778782122249,"version":"3.51.4"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"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":["52375072"],"award-info":[{"award-number":["52375072"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.engappai.2026.114607","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T05:17:38Z","timestamp":1775020658000},"page":"114607","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Semi-supervised domain generalization for fault diagnosis using adaptive pseudo-label selection and distributionally robust optimization"],"prefix":"10.1016","volume":"175","author":[{"given":"Zhikuan","family":"Qi","sequence":"first","affiliation":[]},{"given":"Zhi","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Yonghao","family":"Miao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3380-3785","authenticated-orcid":false,"given":"Shaoping","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.114607_bib1","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.neucom.2020.05.064","article-title":"A deep domain adaption model with multi-task networks for planetary gearbox fault diagnosis","volume":"409","author":"Cao","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.engappai.2026.114607_bib3","article-title":"Semi-supervised medical image segmentation method based on cross-pseudo labeling leveraging strong and weak data augmentation strategies","author":"Chen","year":"2024","journal-title":"arXiv preprint arXiv:2402.11273"},{"issue":"1","key":"10.1016\/j.engappai.2026.114607_bib4","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1109\/TII.2019.2917233","article-title":"Intelligent fault diagnosis for rotary machinery using transferable convolutional neural network","volume":"16","author":"Chen","year":"2020","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"1","key":"10.1016\/j.engappai.2026.114607_bib5","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1109\/TIE.2023.3243293","article-title":"Deep mixed domain generalization network for intelligent fault diagnosis under unseen conditions","volume":"71","author":"Fan","year":"2024","journal-title":"IEEE Trans. Ind. Electron."},{"key":"10.1016\/j.engappai.2026.114607_bib6","article-title":"A multi-source domain information fusion network for rotating machinery fault diagnosis under variable operating conditions","volume":"92","author":"Gao","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.engappai.2026.114607_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122806","article-title":"Causal explaining guided domain generalization for rotating machinery intelligent fault diagnosis","volume":"243","author":"Guo","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114607_bib8","first-page":"1","article-title":"A hybrid generalization network for intelligent fault diagnosis of rotating machinery under unseen working conditions","volume":"70","author":"Han","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.114607_bib9","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/3024590","article-title":"A hybrid matching network for fault diagnosis under different working conditions with limited data","volume":"2022","author":"He","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"10.1016\/j.engappai.2026.114607_bib10","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106396","article-title":"Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions","volume":"207","author":"He","year":"2020","journal-title":"Knowl. Base Syst."},{"key":"10.1016\/j.engappai.2026.114607_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106099","article-title":"Causal disentanglement domain generalization for time-series signal fault diagnosis","volume":"172","author":"Jia","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.engappai.2026.114607_bib12","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.neunet.2021.11.003","article-title":"Cycle-consistent adversarial adaptation network and its application to machine fault diagnosis","volume":"145","author":"Jiao","year":"2022","journal-title":"Neural Netw."},{"issue":"9","key":"10.1016\/j.engappai.2026.114607_bib13","doi-asserted-by":"crossref","first-page":"5965","DOI":"10.1109\/TII.2019.2956294","article-title":"Classifier inconsistency-based domain adaptation network for partial transfer intelligent diagnosis","volume":"16","author":"Jiao","year":"2020","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2026.114607_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102862","article-title":"Latent space alignment based domain adaptation (LSADA) for fault diagnosis of rotating machinery","volume":"62","author":"Kim","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.engappai.2026.114607_bib15","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110491","article-title":"Prior knowledge-embedded meta-transfer learning for few-shot fault diagnosis under variable operating conditions","volume":"200","author":"Lei","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2026.114607_bib16","first-page":"1","article-title":"A new adversarial domain generalization network based on class boundary feature detection for bearing fault diagnosis","volume":"71","author":"Li","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.114607_bib17","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.neucom.2018.05.021","article-title":"A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning","volume":"310","author":"Li","year":"2018","journal-title":"Neurocomputing"},{"key":"10.1016\/j.engappai.2026.114607_bib18","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.neucom.2020.05.014","article-title":"Domain generalization in rotating machinery fault diagnostics using deep neural networks","volume":"403","author":"Li","year":"2020","journal-title":"Neurocomputing"},{"issue":"10","key":"10.1016\/j.engappai.2026.114607_bib19","first-page":"8064","article-title":"Deep semisupervised domain generalization network for rotary machinery fault diagnosis under variable speed","volume":"69","author":"Liao","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.114607_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109493","article-title":"Cross-domain fault diagnosis of bearing using improved semi-supervised meta-learning towards interference of out-of-distribution samples","volume":"252","author":"Lin","year":"2022","journal-title":"Knowl. Base Syst."},{"key":"10.1016\/j.engappai.2026.114607_bib21","article-title":"Intelligent fault diagnosis of rotating machine via expansive dual-attention fusion transformer enhanced by semi-supervised learning","volume":"260","author":"Liu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114607_bib22","doi-asserted-by":"crossref","first-page":"69907","DOI":"10.1109\/ACCESS.2018.2880770","article-title":"A new transfer learning method and its application on rotating machine fault diagnosis under variant working conditions","volume":"6","author":"Qian","year":"2018","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2026.114607_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.111544","article-title":"Variance discrepancy representation: a vibration characteristic-guided distribution alignment metric for fault transfer diagnosis","volume":"217","author":"Qian","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2026.114607_bib24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3154000","article-title":"Conditional contrastive domain generalization for fault diagnosis","volume":"71","author":"Ragab","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"1","key":"10.1016\/j.engappai.2026.114607_bib25","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1109\/TII.2023.3264111","article-title":"Meta-learning based domain generalization framework for fault diagnosis with gradient aligning and semantic matching","volume":"20","author":"Ren","year":"2024","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2026.114607_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109188","article-title":"Domain augmentation generalization network for real-time fault diagnosis under unseen working conditions","volume":"235","author":"Shi","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.engappai.2026.114607_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109402","article-title":"Autoregressive data generation method based on wavelet packet transform and cascaded stochastic quantization for bearing fault diagnosis under unbalanced samples","volume":"138","author":"Sun","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114607_bib28","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102986","article-title":"Pseudo-label guided dual classifier domain adversarial network for unsupervised cross-domain fault diagnosis with small samples","volume":"64","author":"Sun","year":"2025","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.engappai.2026.114607_bib30","doi-asserted-by":"crossref","first-page":"76187","DOI":"10.1109\/ACCESS.2018.2883078","article-title":"Bearing fault diagnosis under variable working conditions based on domain adaptation using feature transfer learning","volume":"6","author":"Tong","year":"2018","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2026.114607_bib31","article-title":"Bearing fault diagnosis based on domain adaptation using transferable features under different working conditions","volume":"2018","author":"Tong","year":"2018","journal-title":"Shock Vib."},{"issue":"9","key":"10.1016\/j.engappai.2026.114607_bib32","doi-asserted-by":"crossref","first-page":"5139","DOI":"10.1109\/TII.2019.2899118","article-title":"A hierarchical deep domain adaptation approach for fault diagnosis of power plant thermal system","volume":"15","author":"Wang","year":"2019","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"5","key":"10.1016\/j.engappai.2026.114607_bib33","first-page":"1047","article-title":"Transfer learning with convolutional neural networks for small sample size problem in machinery fault diagnosis","volume":"9","author":"Xiao","year":"2019","journal-title":"Appl. Sci."},{"key":"10.1016\/j.engappai.2026.114607_bib34","article-title":"Semi-supervised fault diagnosis of machinery using LPS-DGAT under speed fluctuation and extremely low labeled rates","volume":"52","author":"Yan","year":"2022","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.engappai.2026.114607_bib35","doi-asserted-by":"crossref","first-page":"91103","DOI":"10.1109\/ACCESS.2020.2994310","article-title":"Learn generalization feature via convolutional neural network: a fault diagnosis scheme toward unseen operating conditions","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2026.114607_bib36","series-title":"Proceedings of the International Conference on Learning Representations","article-title":"MixUp: beyond empirical risk minimization","author":"Zhang","year":"2017"},{"key":"10.1016\/j.engappai.2026.114607_bib37","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108885","article-title":"Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis","volume":"229","author":"Zhang","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.engappai.2026.114607_bib38","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.engappai.2026.114607_bib39","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.engappai.2026.114607_bib40","first-page":"1","article-title":"Applications of unsupervised deep transfer learning to intelligent fault diagnosis: a survey and comparative study","volume":"70","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626008882?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626008882?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:14:30Z","timestamp":1778778870000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626008882"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":38,"alternative-id":["S0952197626008882"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114607","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Semi-supervised domain generalization for fault diagnosis using adaptive pseudo-label selection and distributionally robust optimization","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114607","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":"114607"}}