{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T03:38:50Z","timestamp":1785123530248,"version":"3.55.0"},"reference-count":41,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100008859","name":"Fuzhou University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008859","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"}]},{"DOI":"10.13039\/501100005270","name":"Fujian Provincial Department of Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005270","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":[[2026,5]]},"DOI":"10.1016\/j.knosys.2026.115696","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T16:33:20Z","timestamp":1772728400000},"page":"115696","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Time & frequency domain consistency generic learning for fault diagnosis of HST bogie via self-supervised contrastive pre-training"],"prefix":"10.1016","volume":"340","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4655-9400","authenticated-orcid":false,"given":"Yuanhong","family":"Chang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujian","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfeng","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhua","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tongyang","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingsong","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinglong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.115696_bib0001","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1109\/TITS.2019.2897583","article-title":"A review of fault detection and diagnosis for the traction system in high-speed trains","volume":"21","author":"Chen","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.knosys.2026.115696_bib0002","doi-asserted-by":"crossref","first-page":"23577","DOI":"10.1109\/JIOT.2024.3385278","article-title":"Two-phase dual-adversarial agents with multivariate information for unsupervised anomaly detection of IIoT-edge devices","volume":"11","author":"Chang","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.knosys.2026.115696_bib0003","doi-asserted-by":"crossref","first-page":"6292","DOI":"10.1109\/TITS.2023.3251341","article-title":"An adaptive multisensor fault diagnosis method for high-speed train bogie","volume":"24","author":"Man","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"128990","key":"10.1016\/j.knosys.2026.115696_bib0004","article-title":"Exploiting attributes and keywords for session-based recommendation with multi-view graph neural network","volume":"296","author":"Chen","year":"2026","journal-title":"Expert. Syst. Appl."},{"issue":"111506","key":"10.1016\/j.knosys.2026.115696_bib0005","article-title":"Industrial process fault diagnosis based on feature enhanced meta-learning toward domain generalization scenarios","volume":"289","author":"Gao","year":"2024","journal-title":"Knowl. Based Syst."},{"issue":"112639","key":"10.1016\/j.knosys.2026.115696_bib0006","article-title":"Uniform bullseye-guided contrastive learning with time-frequency strong augmentation for long-tailed fault diagnosis","volume":"230","author":"Zhong","year":"2025","journal-title":"Mech. Syst. Signal Process"},{"key":"10.1016\/j.knosys.2026.115696_bib0007","doi-asserted-by":"crossref","first-page":"8030","DOI":"10.1109\/TIFS.2025.3594131","article-title":"Citywide multi-step crime prediction via context-aware bayesian tensor decomposition","volume":"20","author":"Liang","year":"2025","journal-title":"IEEE Trans. Inform. Forensics Secur."},{"key":"10.1016\/j.knosys.2026.115696_bib0008","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1109\/TITS.2023.3316793","article-title":"Graph-based model compression for HSR bogies fault diagnosis at IoT edge via adversarial knowledge distillation","volume":"25","author":"Wan","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.knosys.2026.115696_bib0009","doi-asserted-by":"crossref","first-page":"19811","DOI":"10.1109\/JIOT.2022.3168317","article-title":"Intelligent fault quantitative identification for industrial Internet of Things (IIoT) via a novel deep dual reinforcement learning model accompanied with insufficient samples","volume":"9","author":"Chang","year":"2022","journal-title":"IEEE Internet Things J."},{"issue":"110216","key":"10.1016\/j.knosys.2026.115696_bib0010","article-title":"Fault diagnosis of motor bearing in complex scenarios based on Mamba and indicative contrastive learning","volume":"146","author":"Xu","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"103079","key":"10.1016\/j.knosys.2026.115696_bib0011","article-title":"A supervised contrastive learning method based on online complement strategy for long-tailed fine-grained fault diagnosis","volume":"64","author":"Zhao","year":"2025","journal-title":"Adv. Eng. Inform."},{"issue":"108934","key":"10.1016\/j.knosys.2026.115696_bib0012","article-title":"Instance-based transfer learning method via modified domain-adversarial neural network with influence function: applications to design metamodeling and fault diagnosis","volume":"123","author":"Kim","year":"2022","journal-title":"Appl. Soft Comput."},{"issue":"111797","key":"10.1016\/j.knosys.2026.115696_bib0013","article-title":"Instance adaptive multisource transfer for fault diagnosis of rotating machinery under variable working conditions","volume":"202","author":"Shi","year":"2022","journal-title":"Measurement"},{"issue":"110462","key":"10.1016\/j.knosys.2026.115696_bib0014","article-title":"Triplet adversarial learning-driven graph architecture search network augmented with probsparse-attention mechanism for fault diagnosis under few-shot & domain-shift","volume":"199","author":"Chang","year":"2023","journal-title":"Mech. Syst. Signal Process"},{"issue":"103055","key":"10.1016\/j.knosys.2026.115696_bib0015","article-title":"Digital twin-enabled entropy regularized wavelet attention domain adaptation network for gearboxes fault diagnosis without fault data","volume":"64","author":"Zhu","year":"2025","journal-title":"Adv. Eng. Inform."},{"issue":"129149","key":"10.1016\/j.knosys.2026.115696_bib0016","article-title":"Unsupervised model-guided online transfer learning framework for multiple fault detection of satellite control system","volume":"618","author":"Xia","year":"2025","journal-title":"Neurocomputing"},{"issue":"110650","key":"10.1016\/j.knosys.2026.115696_bib0017","article-title":"Pseudo-label assisted contrastive learning model for unsupervised open-set domain adaptation in fault diagnosis","volume":"254","author":"Wang","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"108701","key":"10.1016\/j.knosys.2026.115696_bib0018","article-title":"Efficient temporal flow transformer accompanied with multi-head probsparse self-attention mechanism for remaining useful life prognostics","volume":"226","author":"Chang","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.knosys.2026.115696_bib0019","doi-asserted-by":"crossref","first-page":"5825","DOI":"10.1109\/TITS.2023.3339251","article-title":"A multivariate normal distribution data generative model in small-sample-based fault diagnosis: taking traction circuit breaker as an example","volume":"25","author":"Ma","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"125398","key":"10.1016\/j.knosys.2026.115696_bib0020","article-title":"Intelligent fault diagnosis of rotating machine via Expansive dual-attention fusion Transformer enhanced by semi-supervised learning","volume":"260","author":"Liu","year":"2025","journal-title":"Expert. Syst. Appl."},{"issue":"108026","key":"10.1016\/j.knosys.2026.115696_bib0021","article-title":"CFs-focused intelligent diagnosis scheme via alternative kernels networks with soft squeeze-and-excitation attention for fast-precise fault detection under slow & sharp speed variations","volume":"239","author":"Chang","year":"2022","journal-title":"Knowl. Based Syst."},{"issue":"110597","key":"10.1016\/j.knosys.2026.115696_bib0022","article-title":"Label-guided contrastive learning with weighted pseudo-labeling: a novel mechanical fault diagnosis method with insufficient annotated data","volume":"254","author":"Li","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"102432","key":"10.1016\/j.knosys.2026.115696_bib0023","article-title":"Federated temporal-context contrastive learning for fault diagnosis using multiple datasets with insufficient labels","volume":"60","author":"Zheng","year":"2024","journal-title":"Adv. Eng. Inform."},{"issue":"111507","key":"10.1016\/j.knosys.2026.115696_bib0024","article-title":"Counterfactual-augmented few-shot contrastive learning for machinery intelligent fault diagnosis with limited samples","volume":"216","author":"Liu","year":"2024","journal-title":"Mech. Syst. Signal Process"},{"issue":"123080","key":"10.1016\/j.knosys.2026.115696_bib0025","article-title":"Fault diagnosis of wind turbine gearbox under limited labeled data through temporal predictive and similarity contrast learning embedded with self-attention mechanism","volume":"245","author":"Zhu","year":"2024","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.knosys.2026.115696_bib0026","first-page":"1","article-title":"Efficient estimation of word representations in vector space","author":"Mikolov","year":"2013","journal-title":"Comput. Sci."},{"key":"10.1016\/j.knosys.2026.115696_bib0027","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1","article-title":"Context encoders: feature learning by inpainting","author":"Pathak","year":"2016"},{"key":"10.1016\/j.knosys.2026.115696_bib0028","series-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","first-page":"1","article-title":"Self-supervised dialogue learning","author":"Wu","year":"2019"},{"issue":"102304","key":"10.1016\/j.knosys.2026.115696_bib0029","article-title":"Time-frequency supervised contrastive learning via pseudo-labeling: an unsupervised domain adaptation network for rolling bearing fault diagnosis under time-varying speeds","volume":"59","author":"Pang","year":"2024","journal-title":"Adv. Eng. Inform."},{"issue":"112354","key":"10.1016\/j.knosys.2026.115696_bib0030","article-title":"Granularity knowledge-sharing supervised contrastive learning framework for long-tailed fault diagnosis of rotating machinery","volume":"301","author":"Chang","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115696_bib0031","series-title":"Proceedings of the International Conference on Machine Learning","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"Chen","year":"2020"},{"key":"10.1016\/j.knosys.2026.115696_bib0032","series-title":"Proceedings of the 16th European Conference Computer Vision","first-page":"776","article-title":"Contrastive multiview coding","author":"Tian","year":"2020"},{"key":"10.1016\/j.knosys.2026.115696_bib0033","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"12556","article-title":"Learning to learn single domain generalization","author":"Qiao","year":"2020"},{"key":"10.1016\/j.knosys.2026.115696_bib0034","series-title":"Proceedings of the International Joint Conference on Artificial Intelligence","first-page":"2352","article-title":"Time-series representation learning via temporal and contextual contrasting","author":"Eldele","year":"2021"},{"issue":"109174","key":"10.1016\/j.knosys.2026.115696_bib0035","article-title":"Contrastive-weighted self-supervised model for long-tailed data classification with vision transformer augmented","volume":"177","author":"Hou","year":"2022","journal-title":"Mech. Syst. Signal Process"},{"issue":"110071","key":"10.1016\/j.knosys.2026.115696_bib0036","article-title":"Few-shot learning under domain shift: attentional contrastive calibrated transformer of time series for fault diagnosis under sharp speed variation","volume":"189","author":"Liu","year":"2023","journal-title":"Mech. Syst. Signal Process"},{"key":"10.1016\/j.knosys.2026.115696_bib0037","series-title":"Proceedings of the International Conference on Machine Learning","first-page":"5606","article-title":"CLOCS: contrastive learning of cardiac signals across space, time, and patients","author":"Kiyasseh","year":"2021"},{"key":"10.1016\/j.knosys.2026.115696_bib0038","series-title":"Proceedings of the International Joint Conference on Neural Networks","first-page":"1","article-title":"Self-supervised pre-training for time series classification","author":"Shi","year":"2021"},{"key":"10.1016\/j.knosys.2026.115696_bib0039","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"357","article-title":"CrossViT: cross-attention multi-scale vision transformer for image classification","author":"Chen","year":"2021"},{"key":"10.1016\/j.knosys.2026.115696_bib0040","series-title":"Proceedings of the Association for the Advancement of Artificial Intelligence","first-page":"8980","article-title":"Ts2vec: towards universal representation of time series","volume":"36","author":"Yue","year":"2022"},{"key":"10.1016\/j.knosys.2026.115696_bib0041","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.isatra.2021.10.025","article-title":"Subspace network with shared representation learning for intelligent fault diagnosis of machine under speed transient conditions with few samples","volume":"128","author":"Liu","year":"2022","journal-title":"ISA Trans"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126004363?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126004363?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:14:30Z","timestamp":1777594470000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126004363"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":41,"alternative-id":["S0950705126004363"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115696","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Time & frequency domain consistency generic learning for fault diagnosis of HST bogie via self-supervised contrastive pre-training","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115696","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":"115696"}}