Fault Diagnosis Method for Imbalanced Data Based on Adaptive Diffusion Models and Generative Adversarial Networks

Abstract

In engineering practice, the challenge of collecting fault samples often leads to data imbalance, significantly affecting the performance of fault diagnosis. Data augmentation methods offer an effective solution to address this issue by supplementing fault data. However, these methods must meet the requirements of generating high-quality samples, achieving comprehensive mode coverage, and ensuring fast sample generation. Traditional Generative Adversarial Networks are prone to mode collapse, while diffusion models suffer from slow generation speeds, making it difficult for either to fully satisfy these demands. To overcome these challenges, this paper proposes a data augmentation method for fault diagnosis based on an adaptive diffusion model integrated with Generative Adversarial Networks. By introducing a length-adaptive forward diffusion chain to generate Gaussian mixture noise, this method not only ensures smoother and more stable gradients but also avoids computational redundancy and gradient vanishing problems. At each diffusion timestep, the discriminator learns to distinguish real and generated data across different noise ratios and timesteps, enhancing the diversity of fault samples and effectively mitigating mode collapse. Experimental results on two datasets demonstrate that the proposed method outperforms other data augmentation techniques in terms of generation efficiency and stability, effectively addressing the data imbalance problem and significantly improving fault diagnosis performance.

Keywords: fault diagnosis, Diffusion Model, Generative Adversarial Network, Data imbalance