Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis

Abstract

Numerous intelligent methods have been developed to approach the challenges of fault diagnosis. However, due to the different distributions of training samples and test samples, and the lack of information on test samples, most of these methods cannot directly handle the unsupervised cross-domain fault diagnosis issues. In this paper, a joint distribution adaptation network with adversarial learning is developed to effectively tackle the mentioned fault diagnosis issues. Firstly, deep convolutional neural network (CNN) is constructed to extract the features of training samples and test samples. Secondly, since the joint maximum mean discrepancy (JMMD) cannot precisely measure the joint distribution discrepancy between different domains, an improved joint maximum mean discrepancy (IJMMD) is proposed to accurately match the feature distributions. Finally, adversarial domain adaptation is also developed to help the constructed CNN to extract the domain-invariant features. Therefore, the proposed method can achieve precisely distribution matching, and extract the category-discriminative and domain-invariant features between the source and target domains. Substantial transfer fault diagnosis cases based on three rolling bearing datasets fully demonstrate the effectiveness and generalization ability of the proposed method.