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

doi:10.1109/SMC.2018.00455

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 proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2663-2668
Number of pages	6
ISBN (Electronic)	9781538666500
DOIs	https://doi.org/10.1109/SMC.2018.00455
Publication status	Published - 2 Jul 2018
Externally published	Yes
Event	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan Duration: 7 Oct 2018 → 10 Oct 2018

Publication series

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

Conference

Conference	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Country/Territory	Japan
City	Miyazaki
Period	7/10/18 → 10/10/18

Keywords

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

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10.1109/SMC.2018.00455

Cite this

Baklouti, R., Ben Hamida, A., Mansouri, M., Harkat, M. F., Nounou, H., & Nounou, M. (2018). 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). Article 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

Baklouti, Raoudha ; Ben Hamida, Ahmed ; Mansouri, Majdi et al. / Kernel Generalized Likelihood Ratio Test for Fault Detection of Chemical Processes. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 2663-2668 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

@inproceedings{79282f7533d8446fb4b15915be8ac5c2,

title = "Kernel Generalized Likelihood Ratio Test for Fault Detection of Chemical Processes",

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).",

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

author = "Raoudha Baklouti and {Ben Hamida}, Ahmed and Majdi Mansouri and Harkat, {Mohamed Faouzi} and Hazem Nounou and Mohamed Nounou",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 ; Conference date: 07-10-2018 Through 10-10-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/SMC.2018.00455",

language = "English",

series = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2663--2668",

booktitle = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",

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Baklouti, R, Ben Hamida, A, Mansouri, M, Harkat, MF, Nounou, H & Nounou, M 2018, Kernel Generalized Likelihood Ratio Test for Fault Detection of Chemical Processes. in Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 8616451, Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 2663-2668, 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan, 7/10/18. https://doi.org/10.1109/SMC.2018.00455

Kernel Generalized Likelihood Ratio Test for Fault Detection of Chemical Processes. / Baklouti, Raoudha; Ben Hamida, Ahmed; Mansouri, Majdi et al.
Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 2663-2668 8616451 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Kernel Generalized Likelihood Ratio Test for Fault Detection of Chemical Processes

AU - Baklouti, Raoudha

AU - Ben Hamida, Ahmed

AU - Mansouri, Majdi

AU - Harkat, Mohamed Faouzi

AU - Nounou, Hazem

AU - Nounou, Mohamed

PY - 2018/7/2

Y1 - 2018/7/2

N2 - 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).

AB - 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).

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

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

Kernel Generalized Likelihood Ratio Test for Fault Detection of Chemical Processes

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