A statistical fault detection strategy using PCA based EWMA control schemes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)


In data-based method for fault detection, principal component analysis (PCA) has been used successfully for fault detection in system with highly correlated variables. The aim of this paper is to combine the exponentially weighted moving average (EWMA) control scheme with PCA model in order to improve fault detection performance. In fact, PCA is used to provide a modeling framework for the develop fault detection algorithm. Because of the ability of EWMA control scheme for detecting small changes, this technique is appropriate to improve the detection of a small fault in PCA model. The performance of the PCA-based EWMA fault detection algorithm is illustrated and compared to conventional fault detection methods using simulated continuously stirred tank reactor (CSTR) data. The results show the effectiveness of the developed algorithm.

Original languageEnglish
Title of host publication2013 9th Asian Control Conference, ASCC 2013
Publication statusPublished - 2013
Event2013 9th Asian Control Conference, ASCC 2013 - Istanbul, Turkey
Duration: 23 Jun 201326 Jun 2013


Other2013 9th Asian Control Conference, ASCC 2013



  • CSTR
  • EWMA control scheme
  • Fault detection
  • PCA model

ASJC Scopus subject areas

  • Control and Systems Engineering

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