基于特征解纠缠和联合域对齐的滚动轴承多源域迁移诊断方法

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振动与冲击 ›› 2025, Vol. 44 ›› Issue (1) : 113-120.

故障诊断分析

基于特征解纠缠和联合域对齐的滚动轴承多源域迁移诊断方法

邹松1，董绍江*1,2，夏宗佑1，牟小燕3

作者信息 +

Multi-source domain transfer diagnosis method for rolling bearings based on features disentanglement and joint domain alignment

ZOU Song1, DONG Shaojiang*1,2, XIA Zongyou1, MOU Xiaoyan3

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文章历史 +

摘要

针对变工况环境下采集到的滚动轴承振动数据特征分布不一致及待诊断样本标签难获取，导致轴承故障难诊断的问题，本文提出一种基于特征解纠缠和联合域对齐的滚动轴承多源域迁移诊断方法。首先，为更好提取源域和目标域的通用特征，利用卷积自编码器和正交约束实现域共享特征和域私有特征的解纠缠，筛除域私有特征并保留域共享特征进行域间对齐；其次，为缩小源域与目标域间的特征分布差异，采用多核最大均值差异(Multiple Kernel Maximum Mean Discrepancy, MK-MMD)和相关对齐方法(CORAL)构建融合度量准则；最后，为避免多源域差异带来的负面影响导致诊断精度下降的问题，采用源对抗模块和迁移对抗模块实现源域间及源域与目标域间的域混淆增强，并采用协同决策方式进行特征加权融合，降低弱相关域特征的干扰，实现最终的故障诊断识别。通过两种跨工况下的滚动轴承故障数据集对所提方法开展试验验证，并与单源域诊断方法及其它多源域诊断方法进行了对比分析，证明了所提方法的有效性和优越性。

Abstract

Aiming at the problem that the feature distribution of rolling bearing vibration data collected in variable working conditions is inconsistent and the label of the sample to be diagnosed is difficult to obtain, which leads to the difficulty of bearing fault diagnosis, this paper proposes a multi-source domain transfer diagnosis method of rolling bearing based on feature disentanglement and joint domain alignment. Firstly, in order to better extract the common features of the source domain and the target domain, the convolutional autoencoder and orthogonal constraint are used to disentangle the domain shared features and the domain private features, and the domain private features are filtered out and the domain shared features are retained for inter-domain alignment. Secondly, in order to reduce the feature distribution difference between the source domain and the target domain, the Multiple Kernel Maximum Mean Discrepancy (MK-MMD) and the Correlation Alignment method (CORAL) are used to construct the fusion metric. Finally, in order to avoid the decline of diagnostic accuracy caused by the negative impact of multi-source domain differences, the source adversarial module and the migration adversarial module are used to enhance the domain confusion between the source domain and between the source domain and the target domain, and the collaborative decision-making method is used to perform feature weighted fusion to reduce the interference of weak correlation domain features, and the final fault diagnosis recognition is realized. The proposed method is verified by experiments on rolling bearing fault data sets under two variable working conditions, and compared with the single-source domain diagnosis method and other multi-source domain diagnosis methods, which proves the effectiveness and superiority of the proposed method.

导出引用

邹松1, 董绍江1, 2, 夏宗佑1, 牟小燕3. 基于特征解纠缠和联合域对齐的滚动轴承多源域迁移诊断方法[J]. 振动与冲击, 2025, 44(1): 113-120

ZOU Song1, DONG Shaojiang1, 2, XIA Zongyou1, MOU Xiaoyan3. Multi-source domain transfer diagnosis method for rolling bearings based on features disentanglement and joint domain alignment[J]. Journal of Vibration and Shock, 2025, 44(1): 113-120

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