{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T03:41:18Z","timestamp":1776742878408,"version":"3.51.2"},"reference-count":41,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T00:00:00Z","timestamp":1721865600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62373260"],"award-info":[{"award-number":["62373260"]}],"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":["20231127173014002"],"award-info":[{"award-number":["20231127173014002"]}],"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":["2023JC01021"],"award-info":[{"award-number":["2023JC01021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology Program","award":["62373260"],"award-info":[{"award-number":["62373260"]}]},{"name":"Shenzhen Science and Technology Program","award":["20231127173014002"],"award-info":[{"award-number":["20231127173014002"]}]},{"name":"Shenzhen Science and Technology Program","award":["2023JC01021"],"award-info":[{"award-number":["2023JC01021"]}]},{"name":"Jiangmen Basic and Theoretical Science Research Project","award":["62373260"],"award-info":[{"award-number":["62373260"]}]},{"name":"Jiangmen Basic and Theoretical Science Research Project","award":["20231127173014002"],"award-info":[{"award-number":["20231127173014002"]}]},{"name":"Jiangmen Basic and Theoretical Science Research Project","award":["2023JC01021"],"award-info":[{"award-number":["2023JC01021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The efficient fault detection (FD) of traction control systems (TCSs) is crucial for ensuring the safe operation of high-speed trains. Transient faults (TFs) can arise due to prolonged operation and harsh environmental conditions, often being masked by background noise, particularly during dynamic operating conditions. Moreover, acquiring a sufficient number of samples across the entire scenario presents a challenging task, resulting in imbalanced data for FD. To address these limitations, an unsupervised transfer learning (TL) method via federated Cycle-Flow adversarial networks (CFANs) is proposed to effectively detect TFs under various operating conditions. Firstly, a CFAN is specifically designed for extracting latent features and reconstructing data in the source domain. Subsequently, a transfer learning framework employing federated CFANs collectively adjusts the modified knowledge resulting from domain alterations. Finally, the designed federated CFANs execute transient FD by constructing residuals in the target domain. The efficacy of the proposed methodology is demonstrated through comparative experiments.<\/jats:p>","DOI":"10.3390\/s24154839","type":"journal-article","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T14:31:07Z","timestamp":1721917867000},"page":"4839","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Unsupervised Transfer Learning Method via Cycle-Flow Adversarial Networks for Transient Fault Detection under Various Operation Conditions"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7893-3917","authenticated-orcid":false,"given":"Xiaoyue","family":"Yang","sequence":"first","affiliation":[{"name":"School of Rail Transportation, Wuyi University, Jiangmen 529020, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Long","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Rail Transportation, Wuyi University, Jiangmen 529020, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qidong","family":"Feng","sequence":"additional","affiliation":[{"name":"CRRC Guangdong Railway Vehicles Co., Ltd., Jiangmen 529100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yucheng","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Rail Transportation, Wuyi University, Jiangmen 529020, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sen","family":"Xie","sequence":"additional","affiliation":[{"name":"Institute of Intelligence Science and Engineering, Shenzhen Polytechnic University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1700","DOI":"10.1109\/TITS.2020.3029946","article-title":"Data-driven fault diagnosis for traction systems in high-speed trains: A survey, challenges, and perspectives","volume":"23","author":"Chen","year":"2020","journal-title":"IEEE Trans. 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