A Comprehensive Review of GAN-CNN Hybrid Methods for Intelligent Fault Diagnosis of Rotating Machinery

Abstract

Rotating machinery plays a vital role in modern industry and daily life, with widespread applications across sectors such as aerospace, manufacturing, and transportation. However, prolonged operation under harsh and variable conditions increases the likelihood of mechanical failures, which can lead to system-wide disruptions, costly downtime, and serious safety risks. In recent years, artificial intelligence-based methods, particularly hybrid deep learning approaches, have gained significant attention for their ability to provide timely and accurate fault diagnosis in rotating machinery. Among these, the integration of generative adversarial networks (GANs) and convolutional neural networks (CNNs) has shown promising potential. Despite growing interest, to the best of the authors' knowledge, no comprehensive review has yet been conducted focusing specifically on hybrid fault diagnosis methods that combine GANs and CNNs. This paper aims to fill that gap by providing a thorough review of current research on hybrid GAN-CNN-based fault diagnosis methods for rotating machinery. The review systematically synthesizes 217 peer-reviewed publications, international standards, and authoritative technical resources identified using predefined inclusion and exclusion criteria. The literature search covered two major databases, Scopus and Web of Science, and spanned the period from 2010 to 2026. First, the fundamental principles and architectures of GANs and CNNs, along with their variants, are presented. Next, existing hybrid GAN-CNN methods tailored for fault diagnosis are systematically reviewed, including their improvements and applications in the field. The paper then discusses the advantages and limitations of various GAN and CNN methods. Based on the presented review, existing challenges and future research directions are examined, followed by a conclusion.