Data Augmentation and Fault Diagnosis for Imbalanced Industrial Process Data Based on Residual Wasserstein Generative Adversarial Network With Gradient Penalty

Abstract

In practical industrial applications, equipment usually operates normally and failures are relatively rare, resulting in serious imbalances in the collected data. This imbalance leads to issues such as overfitting, instability, and poor robustness, significantly reducing the accuracy and stability of fault diagnosis system. To address these challenges, this research proposes a method for imbalanced data augmentation and industrial process fault diagnosis based on improved Generative Adversarial Network (GAN). The method adopts Wasserstein distance with gradient penalty and integrates residual connections into the architecture of the generator. This innovation not only helps improve gradient transfer in the generator, but also significantly enhances the data generation capabilities of the generative model through improving the stability of training. Limited industrial process data is used by a generative model to produce synthetic samples with high similarity and diversity. These high-quality samples improve fault diagnosis by enriching the imbalanced dataset. Experimental results on two industrial datasets confirm the method's effectiveness in enhancing fault diagnosis performance with limited data.