Nonlinear Process Fault Detection Based on KPCA and SSA Optimized SVM

doi:10.3969/j.issn.1006-2475.2023.06.003

Abstract

Abstract: To solve the problem of high characteristic dimension of nonlinear data generated by industrial process, a process fault detection algorithm based on Kernel Principal Component Analysis （KPCA） and Sparrow Search Algorithm （SSA） which is used to optimize the parameters of Support Vector Machine is proposed. Firstly, KPCA algorithm is used to extract linear and nonlinear features of industrial data. Secondly, the data after feature extraction is used as training samples to establish a classification SVM model, and SSA algorithm is used to optimize the kernel parameter and penalty factor of SVM. Finally, the optimized SVM model is applied to test samples for fault detection. In this paper, in order to verify the classification effect of the proposed algorithm, KPCA-SSA-SVM is compared with SVM, KPCA-GA-SVM （Genetic Algorithm, GA） by using a set of nonlinear numerical examples and Tennessee Eastman chemical process data, and the efficiency and superiority of the proposed algorithm is verified.

Key words: kernel principal component analysis, sparrow search algorithm, support vector machine, nonlinear process, fault detection

CLC Number:

TP273

SHEN Zhi, LI Yuan. Nonlinear Process Fault Detection Based on KPCA and SSA Optimized SVM[J]. Computer and Modernization, 2023, 0(06): 15-20.

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