A Robust Multi-Granularity Information Fusion Network for Rotating Machinery Fault Diagnosis

Rotating machinery is crucial in industrial production, and its stable operation directly affects both efficiency and safety. However, diverse operating conditions, non-stationary signals, and complex environments pose significant challenges for fault diagnosis. To address these issues, we propose a multi-granularity information fusion network to improve fault classification in complex scenarios. FusionNet first applies Continuous Wavelet Transform to decompose time-series signals, enabling in-depth extraction of key time-frequency features. Next, the multi-granularity feature extraction module focuses on multi-scale and sparse features, dynamically capturing local variations in fault information to enhance and supplement the overall sequence while effectively suppressing noise interference. After that, the Swin-Transformer with window-based interaction adaptively fuses these multi-granularity features, progressively integrating both local details and global dependencies. Finally, the Multi-Granularity Feature Aggregation Module combines features from different scales layer-by-layer and incorporates channel attention to highlight critical fault patterns. Experiments conducted with two datasets, the benchmark CWRU, and a self-developed dataset, show that FusionNet achieves state-of-the-art performance for fault diagnosis. In CWRU, it achieves an average accuracy of 99.67% in four operating conditions. In the self-developed Paper Delivery Structure Coupling dataset, it reaches 99.44%, outperforming conventional models like LSTM by 30.90%.