Generative Adversarial Networks for Vibration Data Generation in Fault Diagnosis of Rotating Machines

Diagnosing faults in rotating machines is crucial for improving overall efficiency and productivity in modern manufacturing industries. However, limited fault samples and class imbalance issues impede the effective development and deployment of fault diagnosis models. Generative adversarial networks (GANs) have emerged as promising solutions to these challenges, and research in this field continues to grow rapidly. However, the latest GAN variants for vibration data generation in fault diagnosis have not been systematically reviewed. Moreover, the existing literature lacks a comprehensive evaluation of the quality metrics used to evaluate GAN‐generated vibration data and inadequately addresses the practical challenges faced in industrial settings. Therefore, this paper reviews the latest studies on the applications of GAN and its variants for generating vibration data in the fault diagnosis of rotating machines. The paper begins with an overview of the basic structure and principles of standard GAN and recent advancements, followed by a detailed analysis of their applications in generating high‐quality synthetic vibration data for fault diagnosis. The review paper then presents the metrics employed to evaluate the quality and diversity of GAN‐generated vibration data. Finally, this review paper presents practical considerations, discusses ongoing research challenges, summarizes recent advancements, and proposes directions for future research.