Fault Detection of the Tennessee Eastman Process using Online Reduced Kernel PCA

Abstract

In this paper, we propose an online reduced kernel principal component analysis (KPCA) method for process monitoring. The developed method consists in updating the KPCA model depending on the dictionary which contains linearly independent kernel functions and then using this new reduced KPCA model for process monitoring. The process monitoring performances are studied using Tennessee Eastman Process (TEP). The results demonstrate the effectiveness of the developed online KPCA technique compared to the classical online KPCA method.