A frequency channel-attention based vision Transformer method for bearing fault identification across different working conditions

Abstract

Fault identification of rolling bearings plays a crucial role in maintaining the efficient and stable operation of equipment. Although traditional fault identification methods have made certain progress, they still lack in model feature extraction capabilities and generalization ability. In this paper, a frequency channel-attention based vision Transformer method is proposed for rolling bearings intelligent fault identification. Using frequency domain channel-attention mechanism, the proposed method is able to preserve fundamental fault information and integrate the frequency characteristics of the vibration signals. The proposed method also leverages the inherent self-attention mechanism of vision Transformer to recognize long-range dependencies within the signal data. This integration of attention not only enhances the model’s sensitivity to signal frequency characteristics but also enables the visualization of the attention mechanism, thereby increasing the model’s interpretability. Additionally, a shift linear layer is proposed to reduce the model’s computational demands while maintaining its robust feature extraction capabilities. This proposed method directly uses the collected vibration raw signals to achieve precise fault identification of rolling bearings, and experimental validation on two datasets demonstrates the model’s diagnostic accuracy under across working conditions.