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

2 Citations (Scopus)


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
Issue number4
Publication statusPublished - 1 Feb 2018
Externally publishedYes



  • fault detection isolation
  • KPCA
  • partial KPCA

ASJC Scopus subject areas

  • Instrumentation

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