Fault diagnosis based on fuzzy support vector machine with parameter tuning and feature selection

This study describes a classification methodology based on support vector machines (SVMs), which offer superior classification performance for fault diagnosis in chemical process engineering. The method incorporates an efficient parameter tuning procedure (based on minimization of radius/margin bound for SVM's leave-one-out errors) into a multi-class classification strategy using a fuzzy decision factor, which is named fuzzy support vector machine (FSVM). The datasets generated from the Tennessee Eastman process (TEP) simulator were used to evaluate the classification performance. To decrease the negative influence of the auto-correlated and irrelevant variables, a key variable identification procedure using recursive feature elimination, based on the SVM is implemented, with time lags incorporated, before every classifier is trained, and the number of relatively important variables to every classifier is basically determined by 10-fold cross-validation. Performance comparisons are implemented among several kinds of multi-class decision machines, by which the effectiveness of the proposed approach is proved.