Unsupervised Joint Subdomain Adaptation Network for Fault Diagnosis

Domain shift is the main reason that the recognition accuracy of traditional intelligent fault diagnosis methods greatly decreases. However, previous methods mainly focus on global domain matching, without considering the finer internal structure and making full use of source domain data, resulting in unsatisfactory adaptive effects. In this paper, we present an unsupervised joint subdomain adaptation network (JSAN), which reduces the discrepancy between the two domains by joint local maximum mean discrepancy (JLMMD). JLMMD divides the features of the specific activation layer into different subdomains according to the labels and matches the subdomains which have the same labels. At the same time, the joint distribution of the feature space and the prediction space of the target domain is matched utilizing the joint distribution of the source domain feature and the label. The experimental results show that the proposed method has achieved remarkable results in the domain adaptation task.