New method of fault diagnosis for rolling bearing imbalance data set based on generative adversarial network

In practical engineering applications, the time of rolling bearings' fault state is very short. Due to the cost, it is unrealistic to work in the fault state for a long time, which will cause the imbalance of fault diagnosis data set. That is the normal samples are far more than the fault samples, and it will greatly affect the accuracy and stability of fault diagnosis. To solve this problem, a fault diagnosis method for bearing imbalance data set was proposed based on Wasserstein distance conditional gradient penalty adversarial generation net Conditional Wasserstein Generative Adversarial Network with gradient penalty (CWGAN-GP), which could stably generate high-quality samples. In the process of fault diagnosis, the quality of the generated samples was evaluated first, and then the unbalanced data set was gradually expanded and balanced. Experiments showed that the proposed method could generate generated samples that were highly similar to the real samples, and the accuracy of fault diagnosis was effectively improved as the unbalanced data set was gradually balanced. In addition, CWGAN-GP model performed better than other generation models in sample generation. © 2022 CIMS. All rights reserved.