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Enhanced generative adversarial networks for bearing imbalanced fault diagnosis of rotating machinery

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Abstract

Traditional rolling bearing fault diagnosis approaches require a large amount of fault data in advance, while some specific fault data is difficult to obtain in engineering scenarios. This imbalanced fault data problem seriously affects the accuracy of fault diagnosis. To improve the accuracy under imbalanced data conditions, we propose a novel data augmentation method of Enhanced Generative Adversarial Networks with Data Selection Module (EGAN-DSM). Firstly, a network enhancement module is designed, which quantifies antagonism between the generator and discriminator through loss value. And the module determines whether to iteratively enhance the networks with weak adversarial ability. Secondly, a Data Selected Module (DSM) is constructed using Hilbert space distance for screening generated data, and the screened data is mixed with original imbalanced data to reconstruct balanced data sets. Then, Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN) is used for fault diagnosis. Finally, the method is verified by data measured on a rotating machine experimental platform. The results show that our method has high fault diagnosis accuracy under the condition of imbalanced data.

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Data Availability

The HENU datasets generated during the current study are available from the corresponding author on reasonable request. The CWRU datasets can be accessed with https://engineering.case.edu/bearingdatacenter/download-data-file

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No.61374134) and in part by the Postgraduate Cultivating Innovation and Quality Improvement Action Plan of Henan University (No.SYLYC2022081).

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Authors and Affiliations

  1. School of Artificial Intelligence, Henan University, 450046, Zhengzhou, China

    Yandong Hou, Jiulong Ma, Jinjin Wang & Tianzhi Li

  2. School of Computer and Information Engineering, Henan University, 475004, Kaifeng, China

    Zhengquan Chen

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  1. Yandong Hou
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Contributions

Yandong Hou: Methodology, Software, Investigation. Jiulong Ma: Methodology, Conceptualization, Supervision, Writing original draft, Software, Investigation. Zhengquan Chen: Validation, Formal analysis, Writing-review and editing. Jinjin Wang: Graphing, Writing-review and editing. Tianzhi Li: Validation, Writing-review and editing.

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Correspondence to Zhengquan Chen.

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Yandong Hou, Jiulong Ma and Zhengquan Chen contributed equally to this work.

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Hou, Y., Ma, J., Wang, J. et al. Enhanced generative adversarial networks for bearing imbalanced fault diagnosis of rotating machinery. Appl Intell 53, 25201–25215 (2023). https://doi.org/10.1007/s10489-023-04870-4

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