Data-augmented patch variational autoencoding generative adversarial networks for rolling bearing fault diagnosis

Abstract

Many recent studies have focused on imbalanced rolling bearing data for fault diagnosis. Complementing the imbalance dataset through data augmentation methods excellently solves this problem superior. In this paper, a patch variational autoencoding generative adversarial network (PVAEGAN) is proposed. Firstly, overlap sampling is designed to preprocess the input samples to alleviate noise interference. Secondly, the PVAEGAN is constructed, and the matrix discriminative output of the model allows it to focus on more features of the data during training. Thirdly, a stability-enhancing structure is designed for PVAEGAN to improve the stability of network parameter variations and inter-network stability for better model results. Furthermore, to verify the use of the multi-class comparison method, experiments are conducted. The results indicate that PVAEGAN can augment imbalanced datasets more effectively and with better robustness than other existing models.