Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty
Joint Authors
Liu, Guifang
Han, Baokun
Jia, Sixiang
Wang, Jinrui
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-21
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
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 a novel framework for imbalanced fault classification based on Wasserstein generative adversarial networks with gradient penalty (WGAN-GP), which interpolates randomly between the true and generated samples to ensure that the transition region between the true and false samples satisfies the Lipschitz constraint.
The process of feature learning is visualized to show the feature extraction process of WGAN-GP.
To verify the availability of the generated samples, a stacked autoencoder (SAE) is set to classify the enhanced dataset composed of the generated samples and original samples.
Furthermore, the exhibition of the loss curve indicates that WGAN-GP has better convergence and faster training speed due to the introduction of the gradient penalty.
Three bearing datasets are employed to verify the effectiveness of the developed framework, and the results show that the proposed framework has an excellent performance in mechanical fault diagnosis under the imbalanced training dataset.
American Psychological Association (APA)
Han, Baokun& Jia, Sixiang& Liu, Guifang& Wang, Jinrui. 2020. Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty. Shock and Vibration،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1212826
Modern Language Association (MLA)
Han, Baokun…[et al.]. Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty. Shock and Vibration No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1212826
American Medical Association (AMA)
Han, Baokun& Jia, Sixiang& Liu, Guifang& Wang, Jinrui. Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty. Shock and Vibration. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1212826
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1212826