Domain Conditioned Joint Adaptation Network for Intelligent Bearing Fault Diagnosis Across Different Positions and Machines

Abstract

In recent years, unsupervised domain adaptation (UDA)-based methods have been widely developed for intelligent bearing fault diagnosis across various working conditions. However, a considerably more challenging and practical fault diagnosis scenario, in which the source and target domains are, respectively, collected from bearings across different positions and machines, is urgent to be addressed. To solve this issue, an innovative end-to-end domain conditioned joint adaptation network (DCJAN), which is composed of a domain conditioned (DC) feature extractor, two classifiers, and a domain discriminator is presented. On the one hand, the DC feature extraction structure is designed to relax totally shared network assumptions in feature extraction and learn more domain-specialized features for cross-domain fault diagnosis of bearings. On the other hand, a joint adaptation strategy is implemented for diagnostic knowledge transfer across domains, in which domain-level and class-level adaptations are, respectively, achieved by domain-adversarial training and bi-classifier adversarial training. Extensive experiments including cross-position fault diagnosis (CPFD) and cross-machine fault diagnosis (CMFD) of bearings indicate the validity and superiority of the proposed method.