Rolling bearings fault diagnosis using a one-dimensional vision transformer with multi-scale residual convolution

Abstract Traditional transformer-based fault diagnosis methods exhibit inherent limitations in extracting local features, making it challenging to effectively identify critical fault characteristics when processing rolling bearing vibration signals, especially in high-noise environments. Therefore, this paper proposes an intelligent diagnosis method based on noise learning, called the multi-scale residual convolution one-dimensional vision transformer (MRC-1DViT). Firstly, the MRC module is constructed using an adaptive noise learning approach to extract local fault features across multiple scales, capturing fault information of varying dimensions, and reduce noise interference. Subsequently, the extracted features are fed into a 1DViT based fault diagnosis model to identify the long-distance dependencies between features. Additionally, contra norm is introduced to mitigate the issue of dimensional collapse in the model. Finally, the effectiveness of MRC-1DViT is validated using the Case Western Reserve University (CWRU) rolling bearing dataset and the Southeast University (SEU) gearbox dataset. Experimental results demonstrate that MRC-1DViT exhibits superior noise robustness and higher stability compared to existing methods. Notably, under a high-noise environment of −6 dB signal-to-noise ratio, the proposed method achieves fault diagnosis accuracies of 91.38% and 89.38%, respectively.