2025 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD 2025)

Accurate fault diagnosis of rolling bearings is critical yet challenging due to severe data imbalance in practical industrial scenarios. To address this issue, we propose a novel adaptive fusion-based multi-domain double-attention diffusion model for imbalanced fault diagnosis. The multi-domain double-attention diffusion leverages a dual-attention mechanism to generate high-quality fault signals, while the adaptive fusion module integrates multi-domain features through cross-attention to enhance representation learning. Extensive experiments on the CWRU dataset demonstrate significant performance improvements, achieving 99.57% accuracy under a severe imbalance ratio of 1:100, confirming the method's effectiveness and generalization capability.
 

Rolling bearing,Fault diagnosis,Data imbalance,Dual-attention mechanism,Multi-domain fusion