Rolling bearing transfer fault diagnosis method based on adversarial variational autoencoder network

The intelligent diagnosis of rolling bearing (RB) faults under different working conditions has attracted significant attention. The two main limitations of existing domain-adaptation-based fault diagnosis methods for RBs are as follows. One is that the source domain transfer fault features contain a large amount of redundant information interfering with domain adaptation. The other is that discrepancies in the distribution between the same class fault samples under different working conditions lead to low transfer diagnosis accuracy. Aiming at overcoming these two limitations, in this study, a cross-domain transfer fault diagnosis model based on Wasserstein adversarial channel compression variational autoencoder (WACCVAE) is proposed. First, fault features are channel-compressed to reduce the interference of redundant features with domain adaptation; an improved variational autoencoder network with a channel compression of fault features—WACCVAE—is proposed. Secondly, the classification module function adopts an inter-class–intra-class distance constraint to improve the distribution alignment ability of same class fault samples for different working conditions. Overall, WACCVAE can accomplish the task of cross-condition transfer fault diagnosis in RBs.