Fault Diagnosis Method for Rolling Bearing Based on Probabilistic Diffusion Models Under Imbalanced Data

Rolling bearing fault diagnosis is significant to the stable operation of rotating machinery systems. However, the fault data collected in practical engineering are seriously imbalanced, which degrades the diagnosis performance. A DRNet intelligent fault diagnosis model was proposed in this article based on the probabilistic diffusion model to enhance fault diagnosis performance under imbalanced data. This article utilized a probabilistic diffusion model to learn a deep semantic representation of fault data, which in turn generates high-quality fault features to balance the number of fault samples. Moreover, we utilized the deep features of fault signals to build a feature metric module that optimized the features of the generated samples. This enhances the accuracy and diversity of the generated data. Finally, the fault categories of the samples were identified using the residual network after data enhancement. The experimental results indicate that compared to other common methods, our method can more effectively fit the feature distribution of real samples resulting in generated samples with higher similarity and exhibiting good stability and accuracy when processing bearing unbalanced fault data.