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 proceedingConference 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 languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3159-3164
Number of pages6
ISBN (Electronic)9781538666500
DOIs
Publication statusPublished - 16 Jan 2019
Event2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan
Duration: 7 Oct 201810 Oct 2018

Publication series

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

Conference

Conference2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
CountryJapan
CityMiyazaki
Period7/10/1810/10/18

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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