A Novel Integrated SVM for Fault Diagnosis Using KPCA and GA

Abstract

Fault diagnosis has been more and more significant in modern factories to ensure the proper functionality of the manufacturing process and to improve product quality and efficiency. A novel integrated SVM model is developed in this paper. Since KPCA can implicitly mapped the data into a higher nonlinear feature space, it is applied in our model to extract key information of original data sets for further training of SVMs. SVM is the main part to detect and identify different kinds of faults, and GA is utilized to optimize the parameters of SVM to avoid over fitting or under fitting problems caused by wrong parameters. Compared with other fault diagnosis methods applied on Tennessee Eastman (TE) process benchmark, we can safely conclude that the proposed model can achieve good predictive accuracy in a relatively short time based on our experiment results.