对抗制
生成语法
计算机科学
样品(材料)
断层(地质)
生成对抗网络
人工智能
机器学习
深度学习
数据挖掘
色谱法
地质学
地震学
化学
作者
Tongyang Pan,Jinglong Chen,Tianci Zhang,Shen Liu,Shuilong He,Haixin Lv
标识
DOI:10.1016/j.isatra.2021.11.040
摘要
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.
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