Design of model fusion learning method based on deep bidirectional GRU neural network in fault diagnosis of industrial processes

Abstract

This paper proposes an end-to-end model fusion feature learning method based on deep bidirectional gated recurrent unit (MCNN-DBiGRU) for fault diagnosis in industrial processes. First, a feature-aligned multi-scale feature extraction model (MCNN) is designed by analyzing the working principles of convolutional and pooling layers of convolutional neural networks. Secondly, a deep bidirectional mechanism is proposed to better extract the time series features in the process data. This mechanism makes the recurrent neural network not only present the forward processing input features from the past to the future, but also the reverse processing from the future to the past. By integrating these features, the diagnostic performance of the network model is improved. To verify that the proposed model has effective diagnostic accuracy for fault diagnosis, we conduct simulation experiments on the Tennessee-Eastman (TE) process and a chemical coking furnace, and compare with several conventional network models. In the end, not only the effectiveness of the model is proved, but it is also confirmed that the model is superior to other conventional neural networks in both diagnostic accuracy and feature robustness.