… This paper attempts to create a predictive framework for machine fault diagnosis tasks, and
modeling data generation process has the possibility to better learn and understand input …

… Application of deep neural network and generative adversarial network to industrial
maintenance: A case study of induction motor fault detection. In Proceedings—IEEE international …

… fault data make the machine learningbased diagnosis methods difficult to carry out. To solve
this problem, this article proposes a new fault diagnosis … -D) generation adversarial network (…

… In this paper, a deep learning-based fault diagnosis method is proposed to address the
imbalanced data problem using generative adversarial networks. Multiple generation modules …

… Aiming at solving the aforementioned problems, a new intelligent failure detection method
for rotating machinery based on wavelet transform, Generative Adversarial Nets (GANs) and …

… detection, but the problem is that the amount of fault data is much … neural network for fault
detection and diagnosis, and compared the oversampling by a generative adversarial network …

… convolutional generative adversarial network (… generative adversarial network (E-GAN)
by integrating DCGAN and an improved CNN, is proposed for rotating machine fault diagnosis…

… published articles related to generative adversarial networks and fault diagnosis from 2017
to … Before 2017, GAN is rarely applied in fault diagnosis, although it has been widely used for …

… problem, which is a hot topic in the field of machine learning. In this study, we re-visit the
imbalanced-class problem for fault detection and diagnosis … The generative adversarial network …

… In this paper, we introduce a method of using the generative adversarial network as the fault
… the machine fault detection model in the training process. We also performed fault detection …