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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/8836477 |
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| _version_ | 1832566162217500672 |
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| author | Baokun Han Sixiang Jia Guifang Liu Jinrui Wang |
| author_facet | Baokun Han Sixiang Jia Guifang Liu Jinrui Wang |
| author_sort | Baokun Han |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d1700f36aa9e4d89bd3772d0fe16c151 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-d1700f36aa9e4d89bd3772d0fe16c1512025-02-03T01:04:59ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/88364778836477Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient PenaltyBaokun Han0Sixiang Jia1Guifang Liu2Jinrui Wang3College of Mechanical and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Mechanical and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Mechanical and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Mechanical and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaRecently, 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.http://dx.doi.org/10.1155/2020/8836477 |
| spellingShingle | Baokun Han Sixiang Jia Guifang Liu Jinrui Wang Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty Shock and Vibration |
| title | Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty |
| title_full | Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty |
| title_fullStr | Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty |
| title_full_unstemmed | Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty |
| title_short | Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty |
| title_sort | imbalanced fault classification of bearing via wasserstein generative adversarial networks with gradient penalty |
| url | http://dx.doi.org/10.1155/2020/8836477 |
| work_keys_str_mv | AT baokunhan imbalancedfaultclassificationofbearingviawassersteingenerativeadversarialnetworkswithgradientpenalty AT sixiangjia imbalancedfaultclassificationofbearingviawassersteingenerativeadversarialnetworkswithgradientpenalty AT guifangliu imbalancedfaultclassificationofbearingviawassersteingenerativeadversarialnetworkswithgradientpenalty AT jinruiwang imbalancedfaultclassificationofbearingviawassersteingenerativeadversarialnetworkswithgradientpenalty |