Fault detection and isolation using interval principal component analysis methods

Tarek Ait Izem, Wafa Bougheloum, Mohamed-Faouzi Harkat, Messaoud Djeghaba

Research output: Contribution to journalConference article

11 Citations (Scopus)


Principal component analysis (PCA) is a commonly used approach to process monitoring. However, it has been developed for singleton variables. Whereas, in many real life cases, this leads to a severe loss of information, this can be overcome by introducing the interval notion. The present paper deals with the study of fault detection and isolations (FDI) of uncertain process using interval PCA. Interval data are generated according to various models, and the FDI procedure is lead using the reconstruction principle technique, in its new interval form, for three interval PCA methods: Vertices PCA, Centers PCA, and Midpoints/Radius PCA. A comparison is presented where it is reported in which conditions each method performs best for FDI purpose.

Original languageEnglish
Pages (from-to)1402-1407
Number of pages6
Issue number21
Publication statusPublished - 1 Sep 2015
Externally publishedYes



  • Fault detection and isolation
  • Interval data
  • Principal component analysis
  • Reconstruction principle

ASJC Scopus subject areas

  • Control and Systems Engineering

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