重庆工商大学学报（自然科学版）2025，Vol.42Issue(2)：49-55,7.DOI:10.16055/j.issn.1672-058X.2025.0002.007
轴承的多域联合适应故障诊断
Multi-domain Joint Adaptive Fault Diagnosis of Bearings
摘要
Abstract
Objective Aiming at the problem of difficulty in feature extraction and domain alignment under different operating conditions of bearings,a multi-domain joint adaptive fault diagnosis method was proposed,which included feature extraction networks,classifiers,and domain discriminators.Methods Considerations were made from the aspects of feature extraction and domain alignment.By utilizing deep separable convolutions and incorporating the idea of attention mechanisms,the DRWNet feature extraction network was constructed to improve the network's capability to extract deep features from vibration signals.Through building multi-domain discriminators,the transferability of different category samples was quantitatively evaluated,and difficult-to-transfer samples were reweighted to fully align the data distributions between the source domain and the target domain,thus improving the diagnostic accuracy of the model.Results Simulation experiments demonstrated that on the Case Western Reserve University bearing dataset,M-DJC achieved diagnostic accuracy of over 99％on 12 transfer tasks.Compared with DANN,CDAN,DDAN,MRAN,and MRDA,M-DJC showed an improvement in diagnostic accuracy ranging from 1.84％to 7.44％,with accelerated convergence speed and enhanced stabilityof the model.Conclusion The M-DJC model not only reduces the noiseimpact in bearing vibration signals but also enhances the domain alignment capability of signal features,better meeting the requirements for bearing fault diagnosis under actual operating conditions.关键词
故障诊断/无监督深度迁移学习/多域鉴别器/特征提取/领域迁移Key words
fault diagnosis/unsupervised deep transfer learning/multi-domain discriminator/feature extraction/domain transfer分类
机械工程引用本文复制引用
李沙沙,赵佰亭,贾晓芬..轴承的多域联合适应故障诊断[J].重庆工商大学学报（自然科学版）,2025,42(2):49-55,7.基金项目
国家自然科学基金面上项目(52174141). （52174141）
