{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T12:35:37Z","timestamp":1786538137153,"version":"build-2736575974"},"reference-count":0,"publisher":"European Alliance for Innovation n.o.","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["EAI Endorsed Trans Energy Web"],"abstract":"<jats:p>INTRODUCTION: Offshore wind turbine gearboxes operate under complex conditions and are highly prone to faults. Traditional single-source diagnostic methods are sensitive to noise and load fluctuations, limiting diagnostic reliability.\r\nOBJECTIVES: This study aims to improve the diagnostic accuracy and recognition performance of gearbox fault categories.\r\nMETHODS: Multi-source monitoring data were preprocessed and fused, followed by feature optimization and construction of a deep learning-based diagnosis model for fault detection and classification.\r\nRESULTS: The proposed model achieved accuracies of 0.951, 0.947, and 0.938 under different load conditions, with Area Under the Curve AUC values above 0.96, outperforming benchmark models.\r\nCONCLUSION: The proposed method improves diagnostic accuracy, robustness, and engineering applicability for offshore wind turbine gearbox fault diagnosis.<\/jats:p>","DOI":"10.4108\/ew.14193","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T11:56:19Z","timestamp":1786535779000},"source":"Crossref","is-referenced-by-count":0,"title":["Fine-grained fault diagnosis methods for offshore wind turbine gearboxes driven by artificial intelligence\u2014by multi-source data analysis and deep learning"],"prefix":"10.4108","volume":"13","author":[{"given":"Hongfeng","family":"Chen","sequence":"first","affiliation":[{"name":"Datang Guoxin Binhai Offshore Wind Power Generation Co., Ltd., China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Chen","sequence":"additional","affiliation":[{"name":"Datang Guoxin Binhai Offshore Wind Power Generation Co., Ltd., China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingdu","family":"Li","sequence":"additional","affiliation":[{"name":"Datang Guoxin Binhai Offshore Wind Power Generation Co., Ltd., China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huihui","family":"Han","sequence":"additional","affiliation":[{"name":"China Datang Group new energy science and Technology Research Institute Co., Ltd., China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinfa","family":"Shi","sequence":"additional","affiliation":[{"name":"Guangzhou Mechanical Engineering Research Institute Co., Ltd., China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"China Datang Group new energy science and Technology Research Institute Co., Ltd., China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2587","published-online":{"date-parts":[[2026,8,12]]},"container-title":["EAI Endorsed Transactions on Energy Web"],"original-title":[],"link":[{"URL":"https:\/\/publications.eai.eu\/index.php\/ew\/article\/download\/14193\/4214","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/publications.eai.eu\/index.php\/ew\/article\/download\/14193\/4214","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T11:56:19Z","timestamp":1786535779000},"score":1,"resource":{"primary":{"URL":"https:\/\/publications.eai.eu\/index.php\/ew\/article\/view\/14193"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,12]]},"references-count":0,"URL":"https:\/\/doi.org\/10.4108\/ew.14193","relation":{},"ISSN":["2032-944X"],"issn-type":[{"value":"2032-944X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,12]]}}}