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However, in practical applications, usually there are multiple available source domains, and relying on diagnostic information from only a single source domain limits the transfer performance. To this end, a non-uniformly weighted multisource domain adaptation network is proposed to address the above challenge. In the proposed method, an intra-domain distribution alignment strategy is designed to eliminate multi-domain shifts and align each pair of source and target domains. Furthermore, a non-uniform weighting scheme is proposed for measuring the importance of different sources based on the similarity between the source and target domains. On this basis, a weighted multisource domain adversarial framework is designed to enhance multisource domain adaptation performance. Numerous experimental results on three datasets validate the effectiveness and superiority of the proposed method.<\/jats:p>","DOI":"10.1007\/s11063-024-11568-2","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T19:02:27Z","timestamp":1709578947000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Non-Uniformly Weighted Multisource Domain Adaptation Network For Fault Diagnosis Under Varying Working Conditions"],"prefix":"10.1007","volume":"56","author":[{"given":"Hongliang","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Yuteng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Haiyang","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,4]]},"reference":[{"issue":"2","key":"11568_CR1","doi-asserted-by":"publisher","first-page":"1486","DOI":"10.1109\/TIE.2020.2970571","volume":"68","author":"G Yu","year":"2021","unstructured":"Yu G, Lin T, Wang Z, Li Y (2021) Time-reassigned multisynchrosqueezing transform for bearing fault diagnosis of rotating machinery. 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