A dynamic weighted joint distribution domain adaptation network for cross-machine fault diagnosis of rolling bearings

作者：Qi Chang, Congcong Fang, Longqing Fan, Wei Zhou, Xianghui Meng · 发表于：Structural Health Monitoring · 年份：2025 · DOI：10.1177/14759217241312080 · 被引用次数：6 · 研究领域：Machine Fault Diagnosis Techniques、Gear and Bearing Dynamics Analysis、Fault Detection and Control Systems

Rolling bearings are fundamental components in machinery, and precise fault diagnosis is critical for assessing the overall reliability of mechanical systems. In cross-domain fault diagnosis, domain drift presents a primary challenge, often leading to performance degradation or model failure, particularly under cross-machine conditions characterized by pronounced distributional discrepancies. This study introduces an innovative framework for cross-machine fault diagnosis, termed the dynamical joint distribution domain adaptation (DJDA) network. In the DJDA framework, a dynamic weighting mechanism is employed to adaptively and quantitatively adjust both overall alignment and individual class discrimination between domains, while simultaneously learning marginal and conditional feature distributions. The multilayer-multiple universal-order moment matching technique is utilized to enhance global distribution alignment between domains, while multiclassifier-assisted adversarial domain adaptation (multiclassifier-GADA) automatically mitigates class discrepancies. Extensive experiments reveal that the proposed framework significantly outperforms state-of-the-art methods and demonstrates promising potential for application in industrial contexts.