EWMA Kernel Generalized Likelihood Ratio Test for Fault Detection of Chemical Processes

Raoudha Baklouti, Ahmed Ben Hamida, Majdi Mansouri, Hazem Nounou, Mohamed Nounou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Fault Detection (FD) is a fundamental step in process monitoring. Owning to its simplicity and effectiveness to deal with nonlinear and highly correlated process variables, kernel principal component analysis (KPCA) has been successfully used in process monitoring. However, the major drawback of this method-based kernel generalized likelihood ratio test (KGLRT) is the neglect of small faults. Inspired by the effectiveness of this detection metric and motivated by the advantages of the univariate exponentially weighted movng average (EWMA), we propose, in this paper, a KPCA-based EWMA-KGLRT FD algorithm. Hence, its performance is illustrated and compared to the conventional KPCA-based KGLRT method through continuously simulated tank reactor (CSTR). In fact, the experimental results confirmed the performance of the proposed algorithm in terms of missed detection (MD) and false alarm (FA) rates.

Original languageEnglish
Title of host publication2020 International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728175133
DOIs
Publication statusPublished - Sept 2020
Externally publishedYes
Event5th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2020 - Sfax, Tunisia
Duration: 2 Sept 20205 Sept 2020

Publication series

Name2020 International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2020

Conference

Conference5th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2020
Country/TerritoryTunisia
CitySfax
Period2/09/205/09/20

Keywords

  • Continuously Stirred Tank Reactor (CSTR)
  • Exponentially Weighted Moving Average (EWMA)
  • Kernel generalized likelihood ratio (KGLRT)
  • fault detection (FD)
  • kernel principal component analysis (KPCA)
  • monitoring

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