DSC-SparseFormer: a lightweight framework for bearing fault diagnosis based on depthwise separable convolution and sparse attention mechanism

Intelligent fault diagnosis methods based on Transformer variants have shown excellent performance. However, these methods generally model features globally, which means a poor anti-noise ability and greater computational complexity. Therefore, a lightweight framework for bearing fault diagnosis based on depthwise separable convolution (DSC) and sparse attention mechanism named DSC-SparseFormer is proposed. First, a guided-and-sparse self-attention (GSSA) module, which can adaptively adjust the region of interest for features, is proposed to extract features, thereby reducing sensitivity to redundant information and downgrading computational complexity. This is potentially the first time such a methodology has been proposed and applied. Second, discrete cosine transform (DCT) is applied to map time-domain signals into frequency domain to enrich feature space, and a time-frequency coupling attention (TFCA) module is developed to dynamically integrate time-domain and frequency-domain features. Finally, extensive experiments on the HUSTBearing dataset, DHU dataset, and HIT dataset demonstrate that DSC-SparseFormer achieves high-accuracy (HUSTBearing: 99.67%, DHU: 99.94%, and HIT: 99.93%) and lightweight (Params: 0.003 M, FLOPs: 6.07 M) fault diagnosis. The effectiveness of the GSSA and TFCA is validated by ablation studies, and feature visualisation is employed to improve the interpretability of the DSC-SparseFormer.