Abstract:

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.

Key words: fault diagnosis, industrial process, convolutional neural network, batch normalization, source domain, target domain, fine-tune, transfer learning