轴承的多域联合适应故障诊断  

Multi-domain Joint Adaptive Fault Diagnosis of Bearings

作　　者：李沙沙 赵佰亭[1] 贾晓芬[1] LI Shasha;ZHAO Baiting;JIA Xiaofen(School of Electrical and Information Engineering,Anhui University of Science and Technology,Anhui Huainan 232001,China)

机构地区：[1]安徽理工大学电气与信息工程学院,安徽淮南232001

出　　处：《重庆工商大学学报(自然科学版)》2025年第2期49-55,共7页Journal of Chongqing Technology and Business University:Natural Science Edition

基　　金：国家自然科学基金面上项目(52174141).

摘　　要：目的针对不同工况下轴承特征提取困难,领域对齐难的问题,提出一种多域联合适应的故障诊断方法,包括特征提取网络、分类器和领域鉴别器。方法从特征提取和领域对齐两方面考虑,通过使用深度可分离卷积,并结合注意力机制的思想构建特征提取网络DRWNet,增强网络对振动信号深层特征的提取能力;通过构建多域鉴别器,对不同类别样本的可传递性进行量化评估,将难以迁移的样本进行重新加权,充分对齐源域和目标域样本间的数据分布,提高模型诊断精度。结果仿真实验表明:在凯斯西储大学轴承数据集上,M-DJC在12个迁移任务上的诊断精度高达99%以上,相较于DANN、CDAN、DDAN、MRAN和MRDA,诊断精度提升了1.84%~7.44%,且模型的收敛速度加快,稳定性提高。结论M-DJC模型既能够降低轴承振动信号中的噪声影响,又能提高信号特征的领域对齐能力,更加符合实际工况下轴承故障诊断的需求。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 stability of the model.Conclusion The M-DJC model not only reduces the noise impact 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 

分 类 号：TH133.3[机械工程—机械制造及自动化]