Fault diagnosis for chemical process based on an improved LSTM neural network

Abstract

Abstract: According to the characteristics of data nonlinearity, high dimension, and dynamic time sequence in modern chemical processes, traditional methods have low accuracy for fault diagnosis. Therefore, a fault diagnosis method based on improved long short-term memory (LSTM) neural network is designed in this paper. First, the collected fault data were input into a convolutional neural network (CNN) to extract the features and reduce the dimension of the data. Secondly, the processed data was input into the improved LSTM network for deep feature extraction. Finally, the extracted deep feature information was input into the attention mechanism for feature “focusing” to realize feature fusion, and then input into the softmax classifier to realize fault classification. Simulation results on the Tennessee-Eastman (TE) dataset demonstrate that the proposed method is better than recurrent neural network (RNN), gated recurrent unit (GRU), CNN, and deep auto-encoder network (DAEN) in fault classification accuracy and diagnose speed, which has certain advantages in the application of the practical chemical process.

Key words: chemical process, deep learning, attention mechanism, fault diagnosis

CLC Number:

TP277

DU Xian-jun, QIU Xiao-yu. Fault diagnosis for chemical process based on an improved LSTM neural network[J]. Journal of Lanzhou University of Technology, 2023, 49(6): 72-79.

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