Reduced Kernel Principal Component Analysis for Fault Detection and Its Application to an Air Quality Monitoring Network

Radhia Fezai; Majdi Mansouri; Okba Taouali; Mohamed-Faouzi Harkat; Hazem Nounou

doi:10.1109/SMC.2018.00535

Reduced Kernel Principal Component Analysis for Fault Detection and Its Application to an Air Quality Monitoring Network

Radhia Fezai, Majdi Mansouri, Okba Taouali, Mohamed-Faouzi Harkat, Hazem Nounou

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

Abstract

Fault detection of nonlinear processes using Kernel Principal Component Analysis(KPCA) method has recently prompt a lot of interest due to its industrial practical importance. However, this method cannot be applied for data sets with a large amount of samples. To overcome this deficiency, this paper proposes a reduced KPCA method based on K-means clustering. This method aims to find a reduced data set among the training data in the input space and uses this reduced data set to built the reduced KPCA model in the feature space. The relevance of the proposed method is illustrated on an air quality monitoring network. The simulation results demonstrate the effectiveness of the new method when compared to the classical KPCA technique.

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	3159-3164
Number of pages	6
ISBN (Electronic)	9781538666500
DOIs	https://doi.org/10.1109/SMC.2018.00535
Publication status	Published - 16 Jan 2019
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	Japan
City	Miyazaki
Period	7/10/18 → 10/10/18

Fingerprint

Keywords

AIRLOR
fault detection
K-means
KPCA
Nonlinear process
PCA

ASJC Scopus subject areas

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

Cite this

Fezai, R., Mansouri, M., Taouali, O., Harkat, M-F., & Nounou, H. (2019). Reduced Kernel Principal Component Analysis for Fault Detection and Its Application to an Air Quality Monitoring Network. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 3159-3164). [8616532] (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.2018.00535

Reduced Kernel Principal Component Analysis for Fault Detection and Its Application to an Air Quality Monitoring Network. / Fezai, Radhia; Mansouri, Majdi; Taouali, Okba; Harkat, Mohamed-Faouzi ; Nounou, Hazem.

Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3159-3164 8616532 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

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

Fezai, R, Mansouri, M, Taouali, O, Harkat, M-F & Nounou, H 2019, Reduced Kernel Principal Component Analysis for Fault Detection and Its Application to an Air Quality Monitoring Network. in Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 8616532, Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 3159-3164, 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan, 7/10/18. https://doi.org/10.1109/SMC.2018.00535

Fezai R, Mansouri M, Taouali O, Harkat M-F , Nounou H. Reduced Kernel Principal Component Analysis for Fault Detection and Its Application to an Air Quality Monitoring Network. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 3159-3164. 8616532. (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). https://doi.org/10.1109/SMC.2018.00535

Fezai, Radhia ; Mansouri, Majdi ; Taouali, Okba ; Harkat, Mohamed-Faouzi ; Nounou, Hazem. / Reduced Kernel Principal Component Analysis for Fault Detection and Its Application to an Air Quality Monitoring Network. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 3159-3164 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

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Access to Document

10.1109/SMC.2018.00535