Integrating feature optimization using a dynamic convolutional neural network for chemical process supervised fault classification

Chemical processes usually exhibit complex, high-dimensional, time-varying, and non-Gaussian char-acteristics, and the diagnosis of faults in chemical processes is particularly important. However, many current fault diagnosis methods do not consider the temporal correlation of process data, feature selection, and feature sequence arrangement. To solve this problem, this paper presents a fault diagnosis method using a dynamic convolutional neural network, based on a genetic algorithm (GA), for optimizing a feature sequence. First, the input data are transformed into a two-dimensional matrix by adding the dimension of time characteristics. Second, the GA is used to select the features, and the sequence of the selected features is optimized. Finally, the optimized feature sequence is input into the convolutional neural network (CNN) to obtain the final diagnosis results. The Tennessee Eastman chemical process is used for experimental analysis, and the proposed model is compared with the weighted cascade forest, deep belief network (DBN), optimized DBN, long short-term memory + CNN and feature selection using random forest models. The experimental results show that the proposed model has higher diagnostic accuracy. The average diagnosis rate of 20 faults is found to be 89.72%. (C) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.