{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:06:06Z","timestamp":1771063566027,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Association of the Third-Line Construction in Sichuan Province","award":["SXJS202324"],"award-info":[{"award-number":["SXJS202324"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Under varying operating conditions, motor bearings undergo continuous changes, necessitating the development of deep learning models capable of robust fault diagnosis. While meta-learning can enhance generalization in low-data scenarios, it is often susceptible to overfitting. Domain adaptation mitigates this by aligning feature distributions across domains; however, most existing methods primarily focus on global alignment, overlooking intra-class subdomain variations. To address these limitations, we propose a novel Dynamic Balance Domain-Adaptation based Few-shot Diagnosis framework (DBDA-FD), which incorporates both global and subdomain alignment mechanisms along with a dynamic balancing factor that adaptively adjusts their relative contributions during training. Furthermore, the proposed framework implicitly leverages the concept of symmetry in feature distributions. By simultaneously aligning global and subdomain-level representations, DBDA-FD enforces a symmetric structure between source and target domains, which enhances generalization and stability under varying operational conditions. Extensive experiments on the CWRU and PU datasets demonstrate the effectiveness of DBDA-FD, achieving 97.6% and 97.3% accuracy on five-way five-shot and three-way five-shot tasks, respectively. Compared to state-of-the-art baselines such as PMML and ADMTL, our method achieves up to 1.4% improvement in accuracy while also exhibiting enhanced robustness against domain shifts and class imbalance.<\/jats:p>","DOI":"10.3390\/sym17091438","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T08:04:15Z","timestamp":1756886655000},"page":"1438","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dynamic Balance Domain-Adaptive Meta-Learning for Few-Shot Multi-Domain Motor Bearing Fault Diagnosis Under Limited Data"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3500-8626","authenticated-orcid":false,"given":"Yanchao","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Imperial College London, London SW7 2AZ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3728-171X","authenticated-orcid":false,"given":"Kunze","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu 610039, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8201-9631","authenticated-orcid":false,"given":"Xiaoliang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu 610039, China"},{"name":"College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China"},{"name":"Department of Computer Science and Operations Research, University of Montreal, Montreal, QC H3C 3J7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1007\/s11831-018-9286-z","article-title":"Condition Monitoring and Fault Diagnosis of Induction Motors: A Review","volume":"26","author":"Choudhary","year":"2018","journal-title":"Arch. 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