A Similarity-Based Multiscale Cluster Region Transfer Network for Cross-Domain Fault Diagnosis of Rolling Bearings

Abstract

Rolling bearings operate under varying working conditions, posing a significant challenge to achieving high-accuracy bearing fault diagnosis. To enhance the fault diagnosis performance of rolling bearings under cross-domain working conditions and noisy environments, this study proposes a similarity-based multiscale cluster region transfer network (SMCRTN), which integrates three core modules: similarity-based processing (SBP), multiscale concatenation U-Net (MCU-Net), and dynamic cluster region transfer (DCRT). Specifically, the SBP module selects source domain samples using entropy score and anomaly score, and optimizes target domain samples through statistical projection. Meanwhile, the MCU-Net incorporates Hilbert-transformed inputs, gated convolution (gated-conv) blocks, and standard convolution blocks to extract multiscale domain-invariant features via dynamic weight adjustment. Furthermore, the DCRT module achieves cross-domain alignment by leveraging cluster-based region transfer and minimizing dynamic entropy-weighted loss. To verify the feasibility and effectiveness of the proposed SMCRTN, comprehensive experiments are performed on the public CWRU dataset and the proprietary PT dataset. Experimental results under various transfer tasks and noise levels demonstrate that the SMCRTN outperforms other intelligent models in terms of diagnostic accuracy and transferability.