Kernel Generalized Likelihood Ratio Test for Fault Detection of Chemical Processes

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

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

Abstract

In this paper, we develop an improved fault detection (FD) technique in order to enhance monitoring abilities of nonlinear chemical processes. Kernel principal component analysis (KPCA) is an effective data driven technique for monitoring nonlinear processes. However, it is well known that data collected from complex and multivariate processes are multiscale due to the variety of changes that could occur in process with different localization in time and frequency. Thus, to enhance process monitoring abilities, we propose to combine advantages of KPCA and multiscale representation using wavelets by constructing a multiscale KPCA model and a new detection chart named multiscale kernel generalized likelihood ratio test (MS-KGLRT) is derived for fault detection. The detection performance of the new chart is studied using the Tennessee Eastman process (TEP).

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2663-2668
Number of pages6
ISBN (Electronic)9781538666500
DOIs
Publication statusPublished - 16 Jan 2019
Event2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan
Duration: 7 Oct 201810 Oct 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conference

Conference2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
CountryJapan
CityMiyazaki
Period7/10/1810/10/18

Keywords

  • Kernel generalized likelihood ratio (KGLRT)
  • Tennessee Eastman process (TEP)
  • fault detection (FD)
  • kernel principal component analysis (KPCA)
  • monitoring
  • multiscale KGLRT

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management
  • Health Informatics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Human-Computer Interaction

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  • Cite this

    Baklouti, R., Ben Hamida, A., Mansouri, M., Harkat, M. F., Nounou, H., & Nounou, M. (2019). Kernel Generalized Likelihood Ratio Test for Fault Detection of Chemical Processes. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 2663-2668). [8616451] (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2018.00455