Fault detection and isolation in nonlinear systems with partial Reduced Kernel Principal Component Analysis method. (February 2018)

• Record Type: Journal Article
• Authors: Jaffel, Ines; Taouali, Okba; Harkat, Mohamed Faouzi; Messaoud, Hassani
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 40:Number 4(2018)
• Page Start: 1289
• Page End: 1296
• Publication Date: 2018-02
• Subjects: KPCA -- RKPCA -- fault detection isolation -- partial KPCA; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/0142331216679250
• Languages: English
• ISSNs: 0142-3312

Abstract

In this article, we suggest an extension of our proposed method in fault detection called Reduced Kernel Principal Component Analysis (RKPCA) (Taouali et al., 2015) to fault isolation. To this end, a set of structured residues is generated by using a partial RKPCA model. Furthermore, each partial RKPCA model was performed on a subset of variables to generate structured residues according to a properly designed incidence matrix. The relevance of the proposed algorithm is revealed on Continuous Stirred Tank Reactor.