Self-supervised Adversarial Network for Intelligent Fault Diagnosis

Abstract

Domain adaptation has significantly promoted the development of intelligent fault diagnosis under variable working conditions. However, previous work developed the transferrable model based on source label information and cross-domain distribution discrepancy, while neglecting the intrinsic correlation of samples. This results in a decrease in transferability. To avoid this drawback, a self-supervised adversarial network (SSAN) is proposed in this paper, which explores sample similarity to guide feature alignment. Experiments on the gearbox dataset demonstrate the promising performance of SSAN in cross-domain fault diagnosis.