Multisource cross-domain fault diagnosis of rolling bearing based on subdomain adaptation network

Abstract

The excellent performance of current intelligent fault diagnosis methods based on deep learning is attributed to the availability of large amounts of labeled data. However, in practical bearing fault diagnosis, the high cost of large sample data and changes in operating conditions lead to the scarcity of available training data, which limits the engineering application of intelligent bearing fault diagnosis. To solve this problem, this paper proposes a cross-domain fault diagnosis method based on multisource subdomain adaptation networks (MSDAN). First, the data from multiple source domains are simultaneously input to a shared feature extractor composed of a one-dimensional residual network. Then, the private feature extractor is used to learn features from different source domains and reduce the domain shifts of each source and target domain using the local maximum mean discrepancy. Finally, the different classifier outputs of the target domain samples are aligned. The highlight of MSDAN is to obtain diagnostic knowledge from multiple source domains and further divide the subdomains using the categories as criteria, which not only aligns the global distribution of the source and target domain but also performs a more refined subdomain alignment. The method effectively alleviates the negative transfer phenomenon caused by insufficient domain alignment in multisource transfer diagnosis. The effectiveness and superiority of the proposed MSDAN method are verified by constructing seven multisource transfer tasks with two bearing fault diagnosis cases, including cross-operating-condition and cross-machine.