Frequency‐Informed Dual‐Channel Neural Network for Bearing Fault Diagnosis

ABSTRACT Deep learning has achieved significant progress in the field of bearing fault diagnosis due to its efficient feature extraction capabilities. However, most data‐driven intelligent diagnosis methods neglect to incorporate domain knowledge, leading to a lack of interpretability. To address this limitation, a dual‐channel fault diagnosis model is proposed in this paper, which includes a time‐domain channel and a frequency‐domain channel network. The two channels can extract features independently, and the diagnostic results are fused into probability values for decision‐making so that the model has better diagnostic performance. In the frequency‐domain channel, a network architecture combining attention mechanisms is proposed based on the characteristics of bearing fault frequencies. A multi‐scale convolution module (MSCM) is employed to extract local features of the spectrum at multiple scales, while a global self‐attention module (GSAM) is utilised to capture global features. Since time‐domain signals only reflect the variation of vibration amplitude over time and cannot directly reveal fault features, a multi‐frequency band feature attention module (MBFAM) is introduced in the time‐domain channel to adaptively focus on different frequency band information. The proposed method integrates frequency information into the network model, providing higher interpretability, which is also confirmed by experimental validation.