{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T14:53:15Z","timestamp":1783003995581,"version":"3.54.5"},"reference-count":62,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Talent Program of Chengdu Technological University","award":["2025RC048"],"award-info":[{"award-number":["2025RC048"]}]},{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan","doi-asserted-by":"publisher","award":["24NSFSC7602"],"award-info":[{"award-number":["24NSFSC7602"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Entropy"],"abstract":"<jats:p>Rolling bearings are essential for modern mechanical equipment and serve in various operational environments. This paper addresses the challenge of vibration data discrepancies in bearings across different operating conditions, which often results in inaccurate fault diagnosis. To tackle this related limitation, a novel lightweight multi-scale attention-based joint adaptive adversarial transfer network, termed MAJATNet, is developed. The proposed network integrates a feature extraction network innovation module with an improved loss function, namely IJA loss. The feature extraction module employs a one-dimensional multi-scale attention residual structure to derive characteristics from monitoring data of source and target domains. IJA loss evaluates the joint distribution discrepancy of high-dimensional features and labels between these domains. IJA loss integrates a joint maximum mean discrepancy (JMMD) loss with a domain adversarial learning loss, which directs the model\u2019s focus toward categorical features while minimizing domain-specific features. The performance and advantages of MAJATNet are demonstrated through cross-domain fault diagnosis experiments using bearing datasets. Experimental results show that the proposed method can significantly improve the accuracy of cross-domain fault diagnosis for bearings.<\/jats:p>","DOI":"10.3390\/e27101011","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T14:50:38Z","timestamp":1758898238000},"page":"1011","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MAJATNet: A Lightweight Multi-Scale Attention Joint Adaptive Adversarial Transfer Network for Bearing Unsupervised Cross-Domain Fault Diagnosis"],"prefix":"10.3390","volume":"27","author":[{"given":"Lin","family":"Song","sequence":"first","affiliation":[{"name":"School of Automobile and Transportation, Chengdu Technological University, Yibin 644012, China"},{"name":"School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1506-351X","authenticated-orcid":false,"given":"Yanlin","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Panzhihua University, Panzhihua 617000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjie","family":"He","sequence":"additional","affiliation":[{"name":"School of Automobile and Transportation, Chengdu Technological University, Yibin 644012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automobile and Transportation, Chengdu Technological University, Yibin 644012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boyang","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Automobile and Transportation, Chengdu Technological University, Yibin 644012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automobile and Transportation, Chengdu Technological University, Yibin 644012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,26]]},"reference":[{"key":"ref_1","first-page":"290","article-title":"Rolling bearing fault diagnosis based on multi-source domain adaptive residual network","volume":"43","author":"Gao","year":"2024","journal-title":"J. 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