Multiscale convolutional conditional domain adversarial network with channel attention for unsupervised bearing fault diagnosis

Abstract

Unsupervised cross-domain fault diagnosis is an effective technical way to realize the engineering application of bearing fault diagnosis methods. However, there are still two problems that need to be resolved. First, the importance of fault features at different scales is generally not consistent. There is redundant information in the fault features. Second, most methods mainly study how to lessen the marginal distribution difference in source and target domains while ignoring their class information. When the data distribution contains complex multimodal structure, this may lead to failure to capture the multimodal structure. To address the above problems, a multiscale channel attention conditional domain adversarial network is proposed. First, a new channel attention module is designed to assign different weights to different channels, which can highlight valuable features and stamp out superfluous features. Then, conditional domain adversarial is used to fully capture the multimodal structure through cross-covariance dependencies between features and classes. Our method’s capability is validated by diagnose results on public data sets and self-built data sets.