Research on fault diagnosis of rolling bearings based on STCSE

Rolling bearings are critical components in rotating machinery, directly influencing equipment safety and reliability. However, early fault signals are typically weak and easily masked by noise, posing significant challenges for accurate fault diagnosis. To address these challenges, this paper proposes a novel dual-branch fault diagnosis framework termed STCSE, which integrates a simplified single-stage Swin Transformer with a CNN enhanced by Squeeze-and-Excitation attention mechanisms. First, raw vibration signals are processed through Variational Mode Decomposition and Fast Fourier Transform, and the extracted features are stacked into a 5 × 1024 time-frequency fusion matrix. Subsequently, two parallel branches extract complementary features: the Swin Transformer branch captures global contextual dependencies (64-day output), while the CNN-SE branch extracts local features with channel attention (128-day output). The fused 192-dimensional representation is then fed into a softmax classifier for fault categorization. Experiments on three benchmark datasets—Case Western Reserve University, Paderborn University, and Southeast University gearbox—demonstrate that STCSE achieves state-of-the-art accuracy of 100.00%, 98.10%, and 96.40% respectively, with only 2.1M parameters and 0.8G FLOPs, significantly outperforming existing methods in both accuracy and computational efficiency. Ablation studies validate the effectiveness of each design component, and noise robustness experiments confirm the model’s practical applicability under varying signal-to-noise ratio conditions.