A novel cross domain diagnosis method based on physical feature weighting and deep residual shrinkage network

Abstract Traditional intelligent diagnostic methods often exhibit poor generalization ability when faced with data from different experimental platforms due to their differing distributions. Current transfer learning methods typically require fine-tuning of the model with some target domain data to adapt to different data distributions. However, in practical applications, it is often difficult to obtain sufficient target domain data. To address this issue, this paper proposes an innovative cross-domain diagnostic method that can diagnose faults on a target platform without relying on its data. The method introduces fault information of bearings into the attention weights of a deep residual shrinkage network (DRSN) through a novel physical feature weighting layer. This effectively extracts the hidden fault information from the signals, which is further refined and classified by DRSN to obtain an accurate fault diagnosis result. Experiments conducted on three bearing datasets validate the effectiveness of the proposed method, demonstrating its promising application prospects for bearing fault diagnosis under different operating conditions.