Fault Diagnosis of Gearbox Bearings Under Extreme Class Imbalance Based on Multi-Resolution Windows and Density-Aware Cross-Modal Fusion
TL;DR
A dual-branch architecture with spatial density-weighted kernels is proposed to decouple high-frequency transients from low-frequency periodic trends and an imbalance-aware strategy integrating Focal Loss and composite augmentation is developed to mitigate model bias.
Abstract
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.