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…

• NN
  National Natural Science Foundation of China

  Awards: 61803227, 61973184
• CP
  China Postdoctoral Science Foundation

  Award: 2023M743530