Swin Transformer for Bearing Fault Diagnosis

Intelligent fault diagnosis requires models that remain robust under diverse and noisy industrial environments. Although vibration signals are widely used for monitoring rotating machinery, they are often collected under varying noise conditions, requiring strong diagnostic capability even in the presence of severe noise. This study proposes a one-dimensional Swin transformer-based model that directly processes raw vibration signals without preprocessing, enabling effective use by non-experts. The proposed model extracts local fault features through self-attention among patches within each window and progressively captures global features via a shift-window mechanism and hierarchical structure, providing a comprehensive representation of fault characteristics. Ablation studies were conducted to design a lightweight yet high-performance model. Extensive evaluations were performed on three public benchmark datasets using comparative models under various noise conditions. Experimental results indicated that the proposed model achieved the highest classification accuracy and superior robustness and computational efficiency in both noise-free and strong-noise environments.