数据失衡下基于WGAN和GAPCNN的轴承故障诊断研究

针对轴承故障数据严重失衡导致所训练的模型诊断能力和泛化能力较差等问题，提出基于Wasserstein距离的生成对抗网络来平衡数据集的方法。该方法首先将少量故障样本进行对抗训练，待网络达到纳什均衡时，再将生成的故障样本添加到原始少量故障样本中起到平衡数据集的作用；提出基于全局平均池化卷积神经网络的诊断模型，将平衡后的数据集输入到诊断模型中进行训练，通过模型自适应地逐层提取特征，实现故障的精确分类诊断。实验结果表明，所提诊断方法优于其他算法和模型，同时拥有较强的泛化能力和鲁棒性。

Research on bearing fault diagnosis based on depth learning under imbalance of data

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.