Online monitoring of nonlinear multiple mode processes based on adaptive local model approach

Abstract

A new adaptive local model based monitoring approach is proposed for online monitoring of nonlinear multiple mode processes with non-Gaussian information. To solve the multiple mode problem, just-in-time-learning (JITL) strategy is introduced. The local least squares support vector regression (LSSVR) model is built on the relevant dataset for prediction. To satisfy the online modeling demand, the real-time problem is considered. Then a two-step independent component analysis–principal component analysis (ICA–PCA) information extraction strategy is introduced to analyze residuals between the real output and the predicted one. Two case studies show that the new proposed method gives better performance compared to conventional methods.