New Adaptive Kernel Principal Component Analysis for Nonlinear Dynamic Process Monitoring

Authors

Chouaib CHAKOUR, Institute of electronics, University Badji Mokhtar - Annaba, P.O. Box 12, Annaba, 23000, AlgeriaFollow
Mohamed Faouzi HARKAT, Institute of electronics, University Badji Mokhtar - Annaba, P.O. Box 12, Annaba, 23000, AlgeriaFollow
Messaoud DJEGHABA, Institute of electronics, University Badji Mokhtar - Annaba, P.O. Box 12, Annaba, 23000, AlgeriaFollow

Author Country (or Countries)

Algeria

Abstract

In this paper a new algorithm for adaptive kernel principal component analysis (AKPCA) is proposed for dynamic process monitoring. The proposed AKPCA algorithm combine two existing algorithms, the recursive weighted PCA (RWPCA) and the moving window kernel PCA algorithms. For fault detection and isolation, a set of structured residuals is generated by using a partial AKPCA models. Each partial AKPCAmodel is performed on subsets of variables. The structured residuals are utilized in composing an isolation scheme, according to a properly designed incidence matrix. The results for applying this algorithm on the nonlinear time varying processes of the Tennessee Eastman shows its feasibility and advantageous performances.

Recommended Citation

CHAKOUR, Chouaib; Faouzi HARKAT, Mohamed; and DJEGHABA, Messaoud (2015) "New Adaptive Kernel Principal Component Analysis for Nonlinear Dynamic Process Monitoring," Applied Mathematics & Information Sciences: Vol. 09: Iss. 4, Article 21.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol09/iss4/21