Generative adversarial network in mechanical fault diagnosis under small sample: A systematic review on applications and future perspectives

Intelligent fault diagnosis has been a promising way for condition-based maintenance. However, the small sample problem has limited the application of intelligent fault diagnosis into real industrial manufacturing. Recently, the generative adversarial network (GAN) is considered as a promising way to solve the problem of small sample. For this purpose, this paper reviews the related research results on small-sample-focused fault diagnosis methods using the GAN. First, a systematic description of the GAN, and its variants, including structure-focused and loss-focused improvements, are introduced in the paper. Second, the paper reviews the related GAN-based intelligent fault diagnosis methods and classifies these studies into three main categories, deep generative adversarial networks for data augmentation, adversarial training for transfer learning, and other application scenarios (including GAN for anomaly detection and semi-supervised adversarial learning). Finally, the paper discusses several limitations of existing studies and points out future perspectives of GAN-based applications.(c) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper reviews the research results on small-sample-focused fault diagnosis methods using generative adversarial networks (GAN), and provides a systematic description of GAN and its variants. The paper also classifies GAN-based intelligent fault diagnosis methods into three categories, including deep generative adversarial networks for data augmentation, adversarial training for transfer learning, and other application scenarios. The limitations of existing studies are discussed, and future perspectives of GAN-based applications in fault diagnosis are pointed out.