CLC number: TP 183

A Fault Diagnosis Model for Complex Industrial Process Based on Improved TCN and 1D CNN

¹ School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201600, China
² State Grid Shanghai Municipal Electric Power Company, Shanghai 200122 , China
³ CSG Smart Science & Technology Co., LTD., Shanghai 201203, China

^† To whom correspondence should be addressed. E-mail: huangbosues@sues.edu.cn

Received: 10 September 2022

Abstract

Fast and accurate fault diagnosis of strongly coupled, time-varying, multivariable complex industrial processes remain a challenging problem. We propose an industrial fault diagnosis model. This model is established on the base of the temporal convolutional network (TCN) and the one-dimensional convolutional neural network (1DCNN). We add a batch normalization layer before the TCN layer, and the activation function of TCN is replaced from the initial ReLU function to the LeakyReLU function. To extract local correlations of features, a 1D convolution layer is added after the TCN layer, followed by the multi-head self-attention mechanism before the fully connected layer to enhance the model's diagnostic ability. The extended Tennessee Eastman Process (TEP) dataset is used as the index to evaluate the performance of our model. The experiment results show the high fault recognition accuracy and better generalization performance of our model, which proves its effectiveness. Additionally, the model's application on the diesel engine failure dataset of our partner's project validates the effectiveness of it in industrial scenarios.