DRSwin-ST: An intelligent fault diagnosis framework based on dynamic threshold noise reduction and sparse transformer with Shifted Windows

In real industrial environments, acquiring vibration data from bearings is often challenging due to noise, resulting in network models that excel when trained on datasets with sufficient samples but struggle with accurate fault identification in real-world scenarios, inevitably threatening the reliability of fault diagnosis. To address this problem, this paper proposes an end-to-end fault diagnosis framework (DRSwin-ST) based on sparse transformer with a shift window and dynamic threshold noise reduction. The Swin-Transformer serves as the backbone, leveraging a multi-head self-attention mechanism with a shift window to capture global information. The 1.5-Entmax replaces Softmax in the self-attention mechanism, sparsifying irrelevant information and allowing the model to focus on essential details. The self-attention mechanism, combined with a multi-scale structure, forms a forward feedback network to obtain rich fault feature information. In addition, the paper integrates a large convolutional kernel and a dynamic soft-threshold noise reduction module to construct a convolutional network in front of the transformer structure. This configuration extracts fault feature information and removes the noise, enhancing the fault recognition accuracy of the model. Experimental results on three diverse datasets demonstrate that DRSwin-ST exhibits robustness and high accuracy even in scenarios with limited samples and high noise, validating its exceptional performance.