Physics-informed diffusion-based augmentation for vibration-based fault diagnosis of rotating machinery

Rotating machinery fault diagnosis is fundamental to the safe and stable operation of smart manufacturing systems. However, vibration-based diagnostic models are often limited by the scarcity of labeled fault data, which weakens their robustness and generalization under varying operating conditions. Recent studies have applied denoising diffusion probabilistic models (DDPMs) to vibration signal augmentation in limited-sample scenarios. Yet, most existing methods are developed on raw time-series signals or generic time-frequency representations, without explicitly incorporating the multiscale impulsive characteristics of fault vibrations. As a result, the generated signals often exhibit weakened fault-related transients. To address this issue, this paper proposes a wavelet-preprocessed temporal-attention diffusion framework for vibration-signal augmentation. Multiscale wavelet priors are integrated into the diffusion backbone to preserve impact-sensitive structures, temporal-attention modules are embedded into the reverse denoising process to enhance temporal dependency modeling, and an asymmetric U-Net with a strengthened decoder is adopted to improve reconstruction quality. Experiments on two bearing test platforms demonstrate that the proposed method generates signals with higher physical fidelity and achieves superior diagnostic performance compared with representative generative baselines. These results indicate that the proposed framework is an effective augmentation strategy for robust vibration-based intelligent diagnostics under data-scarce conditions.