Partial Domain Adaptation Method Based on Class-Weighted Alignment for Fault Diagnosis of Rotating Machinery

Abstract

Domain adaptation (DA)-based methods for fault diagnosis (FD) of rotating machinery have achieved impressive results in recent years. Most methods hold the assumption that the source domain (SD) and target domain (TD) share the same label space, which is not always satisfied in actual situations. A more practical scenario called partial domain adaptation (PDA) needs to be given more attention, where the transferable knowledge learned from a larger SD is applied to a smaller but relevant TD. A PDA method called class-weighted alignment-based transfer network (CWATN) is proposed in this paper to adapt to this scenario. A novel weighting method is designed to adapt the data distributions of shared classes. Except for weighted class-level alignment, global-level feature adaptation is also considered to learn more general transferable knowledge. Moreover, a domain discrepancy learning block is plugged in the shared classifier as a residual block, which could enforce the network to learn and measure the discrepancy between SD and TD explicitly, thus improving the result of DA. Three case studies are implemented to verify the superiority of CWATN. Results demonstrate that the proposed method could obtain better diagnostic performance than the selected competitive methods in the PDA scenario.