Multi-Scale Fusion Network Using Time-Division Fourier Transform for Rolling Bearing Fault Diagnosis

The capacity to diagnose faults in rolling bearings is of significant practical importance to ensure the normal operation of the equipment. Frequency-domain features can effectively enhance the identification of fault modes. However, existing methods often suffer from insufficient frequency-domain representation in practical applications, which greatly affects diagnostic performance. Therefore, this paper proposes a rolling bearing fault diagnosis method based on a Multi-Scale Fusion Network (MSFN) using the Time-Division Fourier Transform (TDFT). The method constructs multi-scale channels to extract time-domain and frequency-domain features of the signal in parallel. A multi-level, multi-scale filter-based approach is designed to extract frequency-domain features in a segmented manner. A cross-attention mechanism is introduced to facilitate the fusion of the extracted time-frequency domain features. The performance of the proposed method is validated using the CWRU and Ottawa datasets. The results show that the average accuracy of MSFN under complex noisy signals is 97.75% and 94.41%. The average accuracy under variable load conditions is 98.68%. This demonstrates its significant application potential compared to existing methods.