改进条件对抗网络在小样本故障诊断中的研究

Research on Improved Conditional Generative Adversarial Network for Small Sample Fault Diagnosis

在实际智能设备的故障诊断中,往往很难获得大量的故障样本,这对基于机器学习的故障诊断的分类精度造成不可估量的影响。为了提高小样本情况下的故障诊断精度,提出一种基于条件对抗网络的生成模型(Conditional generative adversarial networks-gradient penalty,CGAN-GP),用于数据增强来获得充足的故障样本。CGAN-GP利用二维卷积,学习预处理后获得的二维故障样本的分布特性,生成与真实样本相似的样本,并使用Wasserstein距离和梯度惩罚(Gradient penalty,GP)策略解决模型训练中的问题,同时将故障样本的标签信息输入模型引导模型生成特定的故障样本,实现一个模型可生成多种故障样本,并且在CWRU轴承数据集上得以验证。研究表明提出的模型可以生成与真实样本特征相似的高质量样本,能够有效提高小样本情况下故障诊断的识别率。

In the fault diagnosis of practical intelligent equipment,it is often difficult to obtain a large number of fault samples,which has an inestimable impact on the classification accuracy of fault diagnosis based on machine learning.In order to improve the accuracy of fault diagnosis in the case of small samples,a generation model(AGAN-GP)based on conditional generative adversarial networks was proposed for data enhancement to obtain sufficient fault samples.AGAN-GP used two-dimensional convolution to learn the distribution characteristics of two-dimensional fault samples obtained after preprocessing,generated samples similar to real samples,used Wasserstein distance and gradient penalty(GP)strategy to solve the problems in model training,and input the label information of fault samples into the model to guide the model to generate specific fault samples.Implementing a model can generate a variety of fault samples and be verified on CWRU bearing data set.The research result shows that the proposed model can generate high-quality samples with similar characteristics to the real samples,and can effectively improve the recognition rate of fault diagnosis in the case of small samples.