title: Moving window KPCA with reduced complexity for nonlinear dynamic process monitoring.
abstract: This paper proposes an improved Reduced Kernel Principal Component Analysis (RKPCA) for handling nonlinear dynamic systems. The proposed method is entitled Moving Window Reduced Kernel Principal Component Analysis (MW-RKPCA). It consists firstly in approximating the principal components (PCs) of the KPCA model by a reduced data set that approaches "properly" the system behavior in the order to elaborate an RKPCA model. Secondly, the proposed MW-RKPCA consists on updating the RKPCA model using a moving window. The relevance of the proposed MW-RKPCA technique is illustrated on a Tennessee Eastman process.
authors.affiliation: Laboratory of Automatic Signal and Image Processing, National School of Engineers of Monastir, University of Monastir, 5019, Tunisia.
authors.forename: Ines
authors.initials: I
authors.lastname: Jaffel
authors.affiliation: Laboratory of Automatic Signal and Image Processing, National School of Engineers of Monastir, University of Monastir, 5019, Tunisia. Electronic address: taoualiok@gmail.com.
authors.forename: Okba
authors.initials: O
authors.lastname: Taouali
authors.affiliation: Badji Mokhtar - Annaba University, BP.12, Annaba 2300, Algeria.
authors.forename: Mohamed Faouzi
authors.initials: MF
authors.lastname: Harkat
authors.affiliation: Laboratory of Automatic Signal and Image Processing, National School of Engineers of Monastir, University of Monastir, 5019, Tunisia.
authors.forename: Hassani
authors.initials: H
authors.lastname: Messaoud
keywords: Fault detection; KPCA; MW-RKPCA; Nonlinear dynamic process; RKPCA