Channel Attention-Based Conditional Diffusion Model Applied to Fault Diagnosis Under Imbalanced Data

Issues such as data scarcity and data imbalance have long posed significant difficulties in the field of intelligent fault diagnosis. They lead to reduced diagnostic accuracy and endanger the safety and reliability of industrial equipment. To address these challenges, this study introduces a novel channel attention-based conditional diffusion model (CAC-DM) that recalibrates features through a squeeze-and-excitation process. This enhancement boosts the model’s ability to focus on critical features while suppressing irrelevant information, thereby improving the UNet network’s discrimination capability in handling small-sample faults that are highly similar in nature. Experimental validation demonstrates that CAC-DM performs exceptionally well in scenarios with high class similarity, effectively distinguishing among categories with similar distributions in limited data and generating high-quality samples. Compared to existing generative methods, the CAC-DM exhibits significant advantages in producing distinguishable fault samples, particularly in cases of sample imbalance. This approach offers an effective new solution for fault diagnosis.