New fault detection method based on reduced kernel principal component analysis (RKPCA)

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

Research output: Contribution to journalArticle

24 Citations (Scopus)


This paper proposes a new method for fault detection using a reduced kernel principal component analysis (RKPCA). The proposed RKPCA method consists on approximating the retained principal components given by the KPCA method by a set of observation vectors which point to the directions of the largest variances with the retained principal components. The proposed method has been tested on a chemical reactor and the results were satisfactory.

Original languageEnglish
Pages (from-to)1547-1552
Number of pages6
JournalInternational Journal of Advanced Manufacturing Technology
Issue number5-8
Publication statusPublished - 1 Jul 2016
Externally publishedYes



  • Fault detection
  • KPCA
  • PCs
  • SPE

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Control and Systems Engineering
  • Computer Science Applications
  • Software
  • Mechanical Engineering

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