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

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

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 languageEnglish
Title of host publication2018 European Control Conference, ECC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2697-2702
Number of pages6
ISBN (Electronic)9783952426982
DOIs
Publication statusPublished - 27 Nov 2018
Event16th European Control Conference, ECC 2018 - Limassol, Cyprus
Duration: 12 Jun 201815 Jun 2018

Other

Other16th European Control Conference, ECC 2018
CountryCyprus
CityLimassol
Period12/6/1815/6/18

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ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization

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). [8550213] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ECC.2018.8550213