| 英文标题 | Research on bearing fault diagnosis based on WGAN and GAPCNN under imbalance of data |
| 作者英文名 | Xue Zhenze, Man Junfeng, Peng Cheng, Deng He |
| 机构英文名 | 1.School of Computer Science,Hunan University of Technology,Zhuzhou Hunan 412007,China;2.School of Automation,Central South University,Changsha 410083,China;3.School of Changsha Social Work College,Changsha 410004,China |
| 英文摘要 | Aiming at the problem of poor diagnosis ability and generalization ability of the trained model caused by serious imbalance of bearing fault data, this paper proposed a method of generative adversarial networks based on Wasserstein distance to balance dataset. Firstly, it trained a small number of fault samples for adversarial training. Then when the network reached the Nash equilibrium, it added the generated fault samples to the original small number of fault samples to balance the dataset. This paper proposed a diagnostic model based on global average pooled convolutional neural network. The balanced data set was input into the diagnostic model for training. The model was adaptively extracted layer by layer to achieve accurate classification diagnosis of faults. The experimental results show that the proposed diagnostic method is superior to other algorithms and models, and has strong generalization ability and robustness. |
| 英文关键词 | fault diagnosis; deep learning; rolling bearing; generative adversarial networks; convolution neural network |