Abstract: To improve the accuracy of batch process fault diagnosis and address the dependence of traditional classifiers on feature extraction, this paper proposes a fault diagnosis model that combines the improved temporal convolutional network (ITCN), improved dung beetle optimizer (IDBO), and support vector machine (SVM). Fault diagnosis is divided into two processes: fault feature extraction and classification diagnosis. First, ITCN is used to extract features from batch process data, and the outputs of the fully connected layers are taken as the input to the IDBO-SVM classification layers. Second, IDBO is employed to optimize the parameters of SVM to enhance the classification accuracy of the model; meanwhile, t-distributed stochastic neighbor embedding (T-SNE) is used for visual analysis to further verify the model’s feature extraction and classification capabilities. Finally, comparative experiments are conducted on the penicillin fermentation process dataset, where the proposed model is compared with the original temporal convolutional network (TCN) and convolutional neural networks (CNNs). The experimental results show that the proposed model not only improves the accuracy of fault identification but also exhibits excellent generalization performance.

Key words: fault diagnosis, batch process, temporal convolutional network (TCN), support vector machine (SVM), dung beetle optimizer (DBO)