Fault Diagnosis Method Based on Two-Stage GAN for Data Imbalance

Abstract

In the field of fault diagnosis, a large number of sensors are used to reflect the working status of equipment with the increasing complexity of mechanical systems. However, collecting enough fault monitoring data, which leads to the widespread problem of data imbalance and continues effect on the improvement of fault diagnosis accuracy, is difficult for some devices in the actual work process. To improve the performance of fault diagnosis under imbalance, first, this article proposes a generative model based on generative adversarial networks (GANs), the first stage of which uses time-series GAN to overcome the problem, wherein traditional GAN cannot effectively extract the time–series information of sequence data, and the second stage incorporates the characteristics of supervised learning of auxiliary classifier GAN to solve the problem, wherein it cannot generate multimode samples. Second, this article also studies the fault diagnosis method based on two-stage GAN (2S GAN) and applies the classification model combining continuous wavelet transform and ResNet18 to the field of mechanical fault diagnosis. The method includes the data preprocessing stage, data augmentation, and model training stages. Finally, this method is validated by two datasets, and the results show that the absolute increments of the accuracy of this method are improved by 5.70%, 6.34%, 9.08%, and 16.35% in case 1 under four imbalance ratios and by 3.36%, 4.65%, 15.93%, and 26.34% in case 2. Thus, it provides an effective solution to the fault diagnosis problem in the data imbalance state.