Journal Article

Improvement of Generative Adversarial Network and Its Application in Bearing Fault Diagnosis: A Review

Ruan, Diwang, Gühmann, Clemens, Yan, Jianping, Chen, Xuran

2023

Overview

A small sample size and unbalanced sample distribution are two main problems when data-driven methods are applied for fault diagnosis in practical engineering. Technically, sample generation and data augmentation have proven to be effective methods to solve this problem. The generative adversarial network (GAN) has been widely used in recent years as a representative generative model. Besides the general GAN, many variants have recently been reported to address its inherent problems such as mode collapse and slow convergence. In addition, many new techniques are being proposed to increase the sample generation quality. Therefore, a systematic review of GAN, especially its application in fault diagnosis, is necessary. In this paper, the theory and structure of GAN and variants such as ACGAN, VAEGAN, DCGAN, WGAN, et al. are presented first. Then, the literature on GANs is mainly categorized and analyzed from two aspects: improvements in GAN’s structure and loss function. Specifically, the improvements in the structure are classified into three types: information-based, input-based, and layer-based. Regarding the modification of the loss function, it is sorted into two aspects: metric-based and regularization-based. Afterwards, the evaluation metrics of the generated samples are summarized and compared. Finally, the typical applications of GAN in the bearing fault diagnosis field are listed, and the challenges for further research are also discussed.

• Publisher: MDPI AG
• Subject: Artificial intelligence / bearing fault diagnosis / Bearings / Bearings (Machinery) / data augmentation / Datasets / Deep learning / Fault diagnosis / Fault location (Engineering) / GAN review / GAN structure improvement / generative adversarial network (GAN) / Generative adversarial networks / loss function modification / Machine learning / Neural networks / Regularization / Research methodology / Sample size / Technology application / Testing