A Novel Data Augmentation Method Based on Denoising Diffusion Probabilistic Model for Fault Diagnosis Under Imbalanced Data

Imbalanced data constitute a significant challenge in intelligent fault diagnosis cases because they can result in degraded diagnosis accuracy, which can in turn jeopardize the safety and reliability of industrial equipment. Generative adversarial networks (GANs) have been effectively used as common data augmentation methods to address this issue. However, their training process is difficult to perform and prone to mode collapse. Therefore, this article proposes a novel data augmentation method grounded in a diffusion model. The proposed method generates samples through physical simulation rather than adversarial training, which avoids the instability and mode collapse issues faced by GANs, leading to a more stable training process. Moreover, the proposed method utilizes the characteristics of gradual diffusion and random sampling to enhance the authenticity and diversity of sample generation. In addition, in terms of evaluating generation models, most existing works do not have a unified and thorough evaluation framework. Therefore, a comprehensive evaluation framework is proposed to effectively and comprehensively evaluate the performance of data augmentation models. Finally, the proposed method is evaluated using an open-source dataset and two actual testbeds to validate its effectiveness. The experimental results show that our method can generate higher quality and more diverse pseudosamples, and achieve superior fault diagnosis performance under imbalanced data. Specifically, our approach achieves diagnosis accuracies of 97.00%, 96.48%, and 98.30% on the three different datasets, all of which are superior to those of the compared state-of-the-art data augmentation algorithms.