Fault Detection of the Tennessee Eastman Process using Online Reduced Kernel PCA                         ∗

Radhia Fazai; Majdi Mansouri; Okba Taouali; Mohamed Faouzi Harkat; Nassreddine Bouguila; Mohamed Nounou

doi:10.23919/ECC.2018.8550213

Fault Detection of the Tennessee Eastman Process using Online Reduced Kernel PCA ^∗

Radhia Fazai
, Majdi Mansouri^*
, Okba Taouali
, Mohamed Faouzi Harkat
, Nassreddine Bouguila
, Mohamed Nounou

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

In this paper, we propose an online reduced kernel principal component analysis (KPCA) method for process monitoring. The developed method consists in updating the KPCA model depending on the dictionary which contains linearly independent kernel functions and then using this new reduced KPCA model for process monitoring. The process monitoring performances are studied using Tennessee Eastman Process (TEP). The results demonstrate the effectiveness of the developed online KPCA technique compared to the classical online KPCA method.

Original language	English
Title of host publication	2018 European Control Conference, ECC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2697-2702
Number of pages	6
ISBN (Electronic)	9783952426982
DOIs	https://doi.org/10.23919/ECC.2018.8550213
Publication status	Published - 27 Nov 2018
Externally published	Yes
Event	16th European Control Conference, ECC 2018 - Limassol, Cyprus Duration: 12 Jun 2018 → 15 Jun 2018

Publication series

Name	2018 European Control Conference, ECC 2018

Conference

Conference	16th European Control Conference, ECC 2018
Country/Territory	Cyprus
City	Limassol
Period	12/06/18 → 15/06/18

Access to Document

10.23919/ECC.2018.8550213

Cite this

Fazai, R., Mansouri, M., Taouali, O., Harkat, M. F., Bouguila, N., & Nounou, M. (2018). Fault Detection of the Tennessee Eastman Process using Online Reduced Kernel PCA ^∗ In 2018 European Control Conference, ECC 2018 (pp. 2697-2702). Article 8550213 (2018 European Control Conference, ECC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ECC.2018.8550213

Fazai, Radhia ; Mansouri, Majdi ; Taouali, Okba et al. / Fault Detection of the Tennessee Eastman Process using Online Reduced Kernel PCA ^∗ 2018 European Control Conference, ECC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 2697-2702 (2018 European Control Conference, ECC 2018).

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title = " Fault Detection of the Tennessee Eastman Process using Online Reduced Kernel PCA ∗ ",

abstract = "In this paper, we propose an online reduced kernel principal component analysis (KPCA) method for process monitoring. The developed method consists in updating the KPCA model depending on the dictionary which contains linearly independent kernel functions and then using this new reduced KPCA model for process monitoring. The process monitoring performances are studied using Tennessee Eastman Process (TEP). The results demonstrate the effectiveness of the developed online KPCA technique compared to the classical online KPCA method.",

author = "Radhia Fazai and Majdi Mansouri and Okba Taouali and Harkat, \{Mohamed Faouzi\} and Nassreddine Bouguila and Mohamed Nounou",

note = "Publisher Copyright: {\textcopyright} 2018 European Control Association (EUCA).; 16th European Control Conference, ECC 2018 ; Conference date: 12-06-2018 Through 15-06-2018",

year = "2018",

month = nov,

day = "27",

doi = "10.23919/ECC.2018.8550213",

language = "English",

series = "2018 European Control Conference, ECC 2018",

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

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Fazai, R, Mansouri, M, Taouali, O, Harkat, MF, Bouguila, N & Nounou, M 2018, Fault Detection of the Tennessee Eastman Process using Online Reduced Kernel PCA ^∗ in 2018 European Control Conference, ECC 2018., 8550213, 2018 European Control Conference, ECC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 2697-2702, 16th European Control Conference, ECC 2018, Limassol, Cyprus, 12/06/18. https://doi.org/10.23919/ECC.2018.8550213

Fault Detection of the Tennessee Eastman Process using Online Reduced Kernel PCA ^∗ . / Fazai, Radhia; Mansouri, Majdi; Taouali, Okba et al.
2018 European Control Conference, ECC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 2697-2702 8550213 (2018 European Control Conference, ECC 2018).

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

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T1 - Fault Detection of the Tennessee Eastman Process using Online Reduced Kernel PCA ∗

AU - Fazai, Radhia

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AU - Taouali, Okba

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AU - Bouguila, Nassreddine

AU - Nounou, Mohamed

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N2 - In this paper, we propose an online reduced kernel principal component analysis (KPCA) method for process monitoring. The developed method consists in updating the KPCA model depending on the dictionary which contains linearly independent kernel functions and then using this new reduced KPCA model for process monitoring. The process monitoring performances are studied using Tennessee Eastman Process (TEP). The results demonstrate the effectiveness of the developed online KPCA technique compared to the classical online KPCA method.

AB - In this paper, we propose an online reduced kernel principal component analysis (KPCA) method for process monitoring. The developed method consists in updating the KPCA model depending on the dictionary which contains linearly independent kernel functions and then using this new reduced KPCA model for process monitoring. The process monitoring performances are studied using Tennessee Eastman Process (TEP). The results demonstrate the effectiveness of the developed online KPCA technique compared to the classical online KPCA method.

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DO - 10.23919/ECC.2018.8550213

M3 - Conference contribution

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Fazai R, Mansouri M, Taouali O, Harkat MF, Bouguila N, Nounou M. Fault Detection of the Tennessee Eastman Process using Online Reduced Kernel PCA ^∗ In 2018 European Control Conference, ECC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 2697-2702. 8550213. (2018 European Control Conference, ECC 2018). doi: 10.23919/ECC.2018.8550213