Open Access

Huan WangORCiD, Zi-Hao Ren, Junyu Qi

• Rolling bearings are critical components in rotating machinery, and their health directly affects the safety and stability of industrial equipment. To improve fault identification under strong noise and complex operating conditions, this paper proposes a Time–Frequency Decomposition Transformer (TFD-Trans) for robust rolling bearing fault diagnosis. TFD-Trans integrates wavelet transform and Fourier transform within a signal-processing-informed deep architecture. A wavelet-driven frequency decomposition module hierarchically partitions vibration signals into multiple sub-bands, enabling fine-grained multi-scale feature extraction. A Fourier-driven autocorrelation encoding module then models global periodic dependencies in the frequency domain and enhances fault-sensitive representations. Extensive experiments on two real-world rolling bearing datasets show that TFD-Trans consistently achieves higher accuracy and stronger noise robustness than existing mainstream methods across multiple operating and signal-to-noise conditions.