Fault diagnosis of complex chemical process based on multi‐scale ADCRC feature learning

Abstract

The time series and multi‐scale characteristics of complex industrial process data are always important factors affecting the performance of fault diagnosis. In this study, a new fault diagnosis model based on multi‐scale attention dilated causal residual convolution (ADCRC) is proposed. Aiming at the temporal nature of industrial data, the ADCRC module is developed to extract time series features, in which the ADCRC module is composed of dilated causal convolution (DCC), attention mechanism (AM), and residual connection, DCC is used to extract time series features, AM adjusts the weight of features according to attention distribution to obtain more important feature information, and residual connection is used to enhance the training accuracy of model. For the multi‐scale characteristics of original data, MS‐ADCRC model based on ADCRC module is developed for multi‐scale feature extraction, in which multiple ADCRC modules extract multi‐scale features of data in parallel. Finally, the proposed MS‐ADCRC model is tested on the Tennessee‐Eastman data set. Compared with other existing models, the results show that the proposed MS‐ADCRC model has more advantages in fault diagnosis feature learning.