Fault detection and isolation in nonlinear systems with partial Reduced Kernel Principal Component Analysis method

Ines Jaffel, Okba Taouali, Mohamed-Faouzi Harkat, Hassani Messaoud

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)1289-1296
Number of pages8
JournalTransactions of the Institute of Measurement and Control
Volume40
Issue number4
DOIs
Publication statusPublished - 1 Feb 2018
Externally publishedYes

Fingerprint

fault detection
principal components analysis
nonlinear systems
Fault detection
Principal component analysis
Nonlinear systems
isolation
Set theory
set theory
incidence
reactors
matrices

Keywords

  • fault detection isolation
  • KPCA
  • partial KPCA
  • RKPCA

ASJC Scopus subject areas

  • Instrumentation

Cite this

Fault detection and isolation in nonlinear systems with partial Reduced Kernel Principal Component Analysis method. / Jaffel, Ines; Taouali, Okba; Harkat, Mohamed-Faouzi; Messaoud, Hassani.

In: Transactions of the Institute of Measurement and Control, Vol. 40, No. 4, 01.02.2018, p. 1289-1296.

Research output: Contribution to journalArticle

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