Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty

Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty

Recently, generative adversarial networks (GANs) are widely applied to increase the amounts of imbalanced input samples in fault diagnosis. However, the existing GAN-based methods have convergence difficulties and training instability, which affect the fault diagnosis efficiency. This paper develops...

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Bibliographic Details
Main Authors:	Baokun Han, Sixiang Jia, Guifang Liu, Jinrui Wang
Format:	Article
Language:	English
Published:	Hindawi Limited 2020-01-01
Series:	Shock and Vibration
Online Access:	http://dx.doi.org/10.1155/2020/8836477

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