An effective fault diagnosis approach based on optimal weighted least squares support vector machine

Abstract

Fault diagnosis is always a vital technology in the chemical industry and is influenced by a large number of process variables in the practical manufacturing process. Thus, an effective diagnosis method is crucial in practical chemical processes. A novel fault diagnosis method is proposed based on the weighted least squares support vector machine (WLSVM) to deal with the small sample and non‐linear partition data. It provides the strong potential in predicting faults, especially by further employing a suitable particle swarm optimization (PSO) algorithm to determine the most important parameters of the punishment factor and the Gaussian RBF kernel, which has the advantages of high accuracy and low false alarm rate. PSO‐WLSSVM is further accomplished with the data from the classic benchmark test set Tennessee Eastman (TE) process. Also, the models SVM, PSO‐SVM, and PSO‐LSSVM are employed on the TE process to compare the performance of PSO‐WLSSVM. The results show the validity of the proposed approach on the TE process.