Lightweight physical information diffusion model for fault diagnosis of rolling bearings with imbalanced sample

Intelligent fault diagnosis of rolling bearings under data imbalance remains a critical challenge in industrial environments. A Lightweight Physical Information Diffusion Model (LPIDM) is proposed to address the scarcity and imbalanced distribution of fault samples. Firstly, a region-adaptive noise schedule is introduced to replace the conventional linear schedule, enabling targeted augmentation of fault-relevant regions. Secondly, a Depthwise Separable Residual (DSR) structure is incorporated into the U-Net architecture to reduce model complexity and number of parameters. Finally, a multi-objective collaborative optimization loss function is designed to improve time-frequency fidelity of generated signals. The performance of the proposed method is evaluated through the experiments on public and laboratory bearing datasets. The results demonstrate that LPIDM can generate high-quality fault samples, improve diagnostic accuracy and effectiveness under the imbalanced conditions, and offer a practical solution for intelligent fault diagnosis in actual industrial scenarios.