基于迁移BN-CNN框架的小样本工业过程故障诊断

Fault Diagnosis of Few Shot Industrial Process Based on Transfer BN-CNN Framework

针对工业故障诊断过程中训练样本不足导致的诊断性能低下问题,文中以迁移学习和深度学习方法为基础,提出一种迁移BN-CNN(Batch Normalization-Convolutional Neural Network)框架。为了减少网络对初始化方法的依赖,在卷积神经网络中引入批归一化层,对网络的隐藏层进行归一化处理。针对目标域标签数据不充足问题,通过基于样本的迁移学习方法扩充目标域的标记数据量,引入基于模型的迁移学习方法,通过充足的源域数据预训练BN-CNN网络,并利用数据量扩充后的目标域微调该网络部分参数,降低了少量样本训练深度神经网络的难度,得到了更适合目标域的故障诊断模型。采用TE工业数据集对该方法进行对比验证,实验结果表明,文中所提方法对于小样本工业过程故障具有较好的诊断性能,其平均精度值为0.804。

In view of the problem of weak diagnosis performance caused by insufficient training samples in industrial process fault diagnosis,a transfer BN-CNN framework is proposed based on transfer learning and deep learning in this study.In order to reduce the dependence of the network on the initialization method,a batch normalization layer is introduced into the convolution neural network to normalize the hidden layer of the model.To solve the problem of insufficient label data in the target domain,the sample-based transfer learning method is used to expand the labeled data volume of the target domain.By introducing the model based transfer learning method,the BN-CNN network is pre-trained with sufficient source domain data,and some parameters of the network are fine-tuned by using the expanded target domain.The difficulty of training the deep neural network with a small number of samples is reduced,and a fault diagnosis model suitable for target domain is obtained.The comparison experiments on TE industrial data set show that the proposed has good diagnostic performance for small samples of industrial process faults,and its average accuracy is 0.804.