Sensor fault detection based on principal component analysis for interval-valued data

Tarek Ait-Izem, Mohamed-Faouzi Harkat, Messaoud Djeghaba, Frédéric Kratz

Research output: Contribution to journalArticle

1 Citation (Scopus)


Principal component analysis (PCA)-based fault detection and isolation (FDI) is a well-established data-driven diagnosis strategy that has long been praised for its performances. However, it is still not optimal for uncertain systems, mainly since the model uncertainties usually have a significant effect on the reliability of the method. As an alternative solution, modeling with PCA for interval-valued data ensures a better monitoring by apprehending the sensor uncertainties and including them in the modeling phase. This article presents an extension of data-driven PCA fault detection to the case of interval-valued data. The PCA model is built based on the complete information principal component analysis (CIPCA) for interval-valued data, and different fault detection indices are generated based on the squared prediction error (SPE) statistic. A fault detection scheme is proposed based on squared interval norm of residuals vector. The performances of the proposed fault detection scheme are illustrated using a simulation example and a milling machine process, along with a Monte Carlo experiment for validation.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalQuality Engineering
Publication statusAccepted/In press - 4 Dec 2017
Externally publishedYes



  • complete-information PCA
  • fault detection
  • interval-valued data
  • milling machine
  • principal component analysis
  • SPE statistic

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Industrial and Manufacturing Engineering

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