| 引用本文: | 李沙沙,赵佰亭,贾晓芬.轴承的多域联合适应故障诊断(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2025，42（2）：49-55 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 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。 |
| 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 |
| DOI： |
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| 基金项目: |
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| Multi-domain Joint Adaptive Fault Diagnosis of Bearings |
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LI Shasha ZHAO Baiting JIA Xiaofen
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School of Electrical and Information Engineering Anhui University of Science and Technology Anhui Huainan 232001
China
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| 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 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. |
| Key words: fault diagnosis unsupervised deep transfer learning multi-domain discriminator feature extraction domain
transfer |
