Intelligent fault diagnosis method of rolling bearing based on multi-source domain fast adversarial network

Abstract Fault diagnosis of rolling bearings is among the most crucial links in the prognostic and health management of bearings. To solve the problem of single-source domain transfer learning that cannot adapt well to the target domain, a transfer diagnosis method based on multi-source domain fast adversarial network (MSDFAN) is proposed. First, signals from all domains are input into a common subnetwork of fast neural networks to reduce the complexity and network running time of neural networks. Secondly, several adversarial networks are constructed as domain specific feature extractors and then use Higher-order Moment Matching to reduce distribution differences between A and B domains. The two experimental cases of rolling bearing support the effectiveness and superiority of the proposed MSDFAN.