A Novel Method for Imbalanced Fault Diagnosis of Rotating Machinery Based on Generative Adversarial Networks

Abstract

In the real scenario of engineering, the failure time of rotating machinery is generally much less than when it is in a healthy condition. Considering the cost, it is unrealistic to conduct the large-sample and long-time failure tests. This results in the problem of data imbalance in fault diagnosis, i.e., the number of normal samples far exceeds that of the fault ones, which seriously affects the accuracy and stability of fault diagnosis. For the settlement of the above problem, an auxiliary classier Wasserstein generative adversarial network with gradient penalty (ACWGAN-GP) is proposed in this article, which is capable of generating high-quality samples for the minority classes stably utilizing an imbalanced training set. In the experiment of fault diagnosis, the generated samples first go through the availability verification and then are employed to augment the imbalanced data set gradually. The final results show that the proposed method is competent for the generation of data, which is highly similar to real samples, and the accuracy of fault diagnosis has effective improvement as the imbalanced data set is gradually expanded to equilibrium. In addition, the ACWGAN-GP model presents better performance in sample generation than other widely used methods.