Class Subdomain Adaptation Network for Bearing Fault Diagnosis Under Variable Working Conditions

An innovative class subdomain adaptation network (CSAN) is proposed in response to the problem that the distribution of bearing data is inconsistent under variable working conditions, and the network trained on data from the source working condition cannot be applied to the target working condition. The proposed CSAN model is divided into two components. The first component is a lightweight channel convolution neural network (CCNN) designed to fully utilize channel information and perform feature extraction and classification. The second component is an innovative domain adaptation algorithm that can be used as a loss function embedded in a deep learning network. The embedded domain adaptation loss function consists of two terms. The first is a class-domain loss term, which achieves domain adaptation by measuring the correlation alignment (CORAL) distance between the source domain and target domain in each subdomain divided by category. The second term is a class-margin loss term. Through the idea of maximizing the probability difference, it can not only lessen the unreliability of pseudolabels generated by the network, but also maximize the feature differences among classes to achieve better classification performance. A multitask experiment under changeable working conditions on three public bearing datasets proves the superiority of the proposed CSAN model. Compared with other methods, CSAN can achieve the optimal best performance and has desirable generalization performance, whose value remains near the best-performance value. Hence, CSAN is relatively stable and reliable, proving that it has universal and practical application in bearing fault diagnosis under variable working conditions.