Fault Diagnosis of Gearbox Bearings Under Extreme Class Imbalance Based on Multi-Resolution Windows and Density-Aware Cross-Modal Fusion
Robust bearing fault diagnosis under variable speeds and extreme class imbalance remains challenging due to the difficulty in decoupling transient-periodic features. To address this, we propose a Dual-Branch Weighted Convolution Network with Cross-Attention Fusion (DBWC). Specifically, a dual-branch architecture with spatial density-weighted kernels is proposed to decouple high-frequency transients from low-frequency periodic trends. Subsequently, a Cross-Attention Fusion module synthesizes these heterogeneous features by using global contexts to filter local noise. Additionally, an imbalance-aware strategy integrating Focal Loss and composite augmentation is developed to mitigate model bias. Extensive experiments on MCC5-THU and HUST benchmarks demonstrate that DBWC achieves an accuracy of 91.00% and 90.12%, respectively. The proposed method outperforms state-of-the-art models by an average margin of 5%, providing a data-efficient paradigm for complex industrial monitoring.