Weighted kernel principal component analysis based on probability density estimation and moving window and its application in nonlinear chemical process monitoring

Abstract

Kernel principal component analysis (KPCA) has been widely used in nonlinear process monitoring; however, KPCA does not always perform efficiently because useful information may be submerged under retained KPCs. To address this shortcoming, probability density estimation- and moving weighted window-based KPCA (PM-WKPCA) is proposed. PM-WKPCA is used mainly to estimate the probability and evaluate the importance of each KPC by kernel density estimation and then set different weighting values on KPCs to highlight the useful information. The status of the process is also evaluated comprehensively using weighted statistics within a moving window. The efficiency of the proposed method is demonstrated by the following: case studies on a numerical nonlinear system, the simulated continuously stirred tank reactor process, and the Tennessee Eastman process. Monitoring results indicate that the proposed method is superior to the conventional PCA, KPCA, and some typical extension methods.