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

Back

Conference proceeding

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

Radhia Fazai, Majdi Mansouri, Okba Taouali, Mohamed-Faouzi Harkat, Nassreddine Bouguila and Mohamed Nounou

The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, p.2697

01/01/2018

Abstract

Control charts

Fault detection

Kernel functions

Monitoring

Principal components analysis

Process controls

Conference Title: 2018 17th European Control Conference (ECC) Conference Start Date: 2018, June 12 Conference End Date: 2018, June 15 Conference Location: Limassol, Cyprus 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.

Metrics

1 Record Views

Details

Title: Fault Detection of the Tennessee Eastman Process using Online Reduced Kernel PCA
Creators - without role: Radhia Fazai
Majdi Mansouri
Okba Taouali
Mohamed-Faouzi Harkat
Nassreddine Bouguila
Mohamed Nounou
Publication Details: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, p.2697
Publisher: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Identifiers: 9933626308331
Academic Unit: University of Tabuk
Language: English
Resource Type: Conference proceeding