A novel cross-bearing fault diagnosis method based on pseudo-label transitive domain adaptation networks

Abstract

Transfer across bearings produces a greater domain shift than transfer across working conditions (WCs). Because different bearings may have differences in structural parameters, measurement environments, and WCs, a direct transfer may significantly lower the diagnostic accuracy of the target bearing. A novel fault diagnosis method based on pseudo-label transitive domain adaptation networks (PLTDANs) is proposed to address this problem. First, empirical selection criteria are offered to ensure that appropriate intermediate domains are selected and can better bridge the source and target domains. The intermediate-domain-added TDAN can split the direct transfer process into source-intermediate and intermediate-target transfer diagnoses. This division allows for the gradual correction of the domain shift. Second, the cross-domain pseudo-label constraint (CDPLC) is proposed to select high-confidence intermediate domain samples and generate corresponding pseudo-labels. Pseudo-labels highlight the health status of intermediate domain samples. The application of CDPLC aims to minimize the cumulative error of the TDAN. A cross-bearing fault diagnosis experiment demonstrated the PLTDAN's effectiveness.