To effectively improve the fault detection performance of support vector machine(SVM)in industrial processes,a fault detection method of multi-modal process based on multi-model SVM(MM-SVM)was proposed.Firstly,the local probability density method was applied to preprocess the multi-modal data to eliminate the influence of the multi-modal data on fault detection performance.Then,multiple SVM models for fault classification were established by changing the kernel parameters of SVM.Finally,the classification results of multiple SVM models were integrated,and the data category was defined by the probability to achieve effective fault detection.The proposed method was applied to a multi-modal numerical example and the Tennessee-Eastman multi-modal process.Compared with PCA,KPCA and SVM,the experimental results further verify the effectiveness of the proposed method.

Fault Detection in Chemical Process Based on Sub-Block Canonical Variate Analysis [J]. Journal of Shenyang University of Chemical Technology, 2024, 38(1): 61-70.