ReF-DDPM: A novel DDPM-based data augmentation method for imbalanced rolling bearing fault diagnosis

Abstract

Effective bearing fault diagnosis is crucial to ensure the safety and reliability of mechanical systems. Due to the complex and harsh working environment, mechanical data often comes from imbalanced datasets, which is a pressing problem in diagnosis applications. However, currently proposed data augmentation methods mainly based on generative adversarial networks, remain challenging in balancing the quality and diversity of the generation samples. To solve it, this paper proposes a new data enhancement method called the reparameterized residual denoising diffusion probability model (ReF-DDPM) and applies it to fault diagnosis. The proposed architecture includes a forward diffusion process and a reverse denoising process, where Gaussian noise and original samples are transformed by Markov chains. To improve the quality of generation samples, the noise prediction network is modified for better feature representation by enhancing intra-level and inter-level features. Furthermore, signal labels are added to the model as conditional information to direct the generation of relevant category samples during the sampling process. The study provides a new data augmentation method for bearing imbalanced data, and generation data can be further used for fault diagnosis tasks. Verification experiments demonstrate the effectiveness and good generalization of the method, and improve the accuracy of imbalance fault diagnosis.