{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:18:31Z","timestamp":1767183511361,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T00:00:00Z","timestamp":1700697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52271303","2019J12GX023","2023M730454","3132023627","2021JJ11CG004","2022JJ11CG010","62602010210"],"award-info":[{"award-number":["52271303","2019J12GX023","2023M730454","3132023627","2021JJ11CG004","2022JJ11CG010","62602010210"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Dalian Science Technology Innovation Fund","award":["52271303","2019J12GX023","2023M730454","3132023627","2021JJ11CG004","2022JJ11CG010","62602010210"],"award-info":[{"award-number":["52271303","2019J12GX023","2023M730454","3132023627","2021JJ11CG004","2022JJ11CG010","62602010210"]}]},{"name":"China Postdoctoral Science Foundation","award":["52271303","2019J12GX023","2023M730454","3132023627","2021JJ11CG004","2022JJ11CG010","62602010210"],"award-info":[{"award-number":["52271303","2019J12GX023","2023M730454","3132023627","2021JJ11CG004","2022JJ11CG010","62602010210"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["52271303","2019J12GX023","2023M730454","3132023627","2021JJ11CG004","2022JJ11CG010","62602010210"],"award-info":[{"award-number":["52271303","2019J12GX023","2023M730454","3132023627","2021JJ11CG004","2022JJ11CG010","62602010210"]}]},{"name":"Science and Technology Innovation Fund of Dalian","award":["52271303","2019J12GX023","2023M730454","3132023627","2021JJ11CG004","2022JJ11CG010","62602010210"],"award-info":[{"award-number":["52271303","2019J12GX023","2023M730454","3132023627","2021JJ11CG004","2022JJ11CG010","62602010210"]}]},{"name":"Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime University","award":["52271303","2019J12GX023","2023M730454","3132023627","2021JJ11CG004","2022JJ11CG010","62602010210"],"award-info":[{"award-number":["52271303","2019J12GX023","2023M730454","3132023627","2021JJ11CG004","2022JJ11CG010","62602010210"]}]},{"name":"Innovative Projects for the Application of Advance Research on Equipment","award":["52271303","2019J12GX023","2023M730454","3132023627","2021JJ11CG004","2022JJ11CG010","62602010210"],"award-info":[{"award-number":["52271303","2019J12GX023","2023M730454","3132023627","2021JJ11CG004","2022JJ11CG010","62602010210"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>While deep learning has found widespread utility in gearbox fault diagnosis, its direct application to wind turbine gearboxes encounters significant hurdles. Disparities in data distribution across a spectrum of operating conditions for wind turbines result in a marked decrease in diagnostic accuracy. In response, this study introduces a tailored dynamic conditional adversarial domain adaptation model for fault diagnosis in wind turbine gearboxes amidst cross-condition scenarios. The model adeptly adjusts the importance of aligning marginal and conditional distributions using distance metric factors. Information entropy parameters are also incorporated to assess individual sample transferability, prioritizing highly transferable samples during domain alignment. The amalgamation of these dynamic factors empowers the approach to maintain stability across varied data distributions. Comprehensive experiments on both gear and bearing data validate the method\u2019s efficacy in cross-condition fault diagnosis. Comparative outcomes demonstrate that, when contrasted with four advanced transfer learning techniques, the dynamic conditional adversarial domain adaptation model attains superior accuracy and stability in multi-transfer tasks, making it notably suitable for diagnosing wind turbine gearbox faults.<\/jats:p>","DOI":"10.3390\/s23239368","type":"journal-article","created":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T11:31:25Z","timestamp":1700739085000},"page":"9368","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Dynamic Condition Adversarial Adaptation for Fault Diagnosis of Wind Turbine Gearbox"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3172-600X","authenticated-orcid":false,"given":"Hongpeng","family":"Zhang","sequence":"first","affiliation":[{"name":"Marine Engineering College, Dalian Maritime University, No. 1 Linghai Road, Dalian 116000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinran","family":"Wang","sequence":"additional","affiliation":[{"name":"Marine Engineering College, Dalian Maritime University, No. 1 Linghai Road, Dalian 116000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cunyou","family":"Zhang","sequence":"additional","affiliation":[{"name":"Marine Engineering College, Dalian Maritime University, No. 1 Linghai Road, Dalian 116000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"Marine Engineering College, Dalian Maritime University, No. 1 Linghai Road, Dalian 116000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jizhe","family":"Wang","sequence":"additional","affiliation":[{"name":"Marine Engineering College, Dalian Maritime University, No. 1 Linghai Road, Dalian 116000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1307-9507","authenticated-orcid":false,"given":"Guobin","family":"Li","sequence":"additional","affiliation":[{"name":"Marine Engineering College, Dalian Maritime University, No. 1 Linghai Road, Dalian 116000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8549-3382","authenticated-orcid":false,"given":"Chenzhao","family":"Bai","sequence":"additional","affiliation":[{"name":"Marine Engineering College, Dalian Maritime University, No. 1 Linghai Road, Dalian 116000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"147481","DOI":"10.1109\/ACCESS.2021.3124025","article-title":"Fault Diagnosis Methods Based on Machine Learning and its Applications for Wind Turbines: A Review","volume":"9","author":"Sun","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5990","DOI":"10.1109\/TIE.2017.2774777","article-title":"A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method","volume":"65","author":"Wen","year":"2018","journal-title":"IEEE Trans. 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