Online process monitoring based on kernel method

Radhia Fezai, Ines Jaffel, Okba Taouali, Mohamed-Faouzi Harkat, Nasreddine Bouguila

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

2 Citations (Scopus)

Abstract

This paper discusses the monitoring of dynamic process. In recent years, Kernel Principal component analysis (KPCA) has gained significant attention as a monitoring method of nonlinear systems. However, the fixed KPCA model limit its application for dynamic systems. For this purpose a new Variable Moving Window Kernel PCA (VMWKPCA) method is introduced to update the KPCA model. The basic idea of this technique is to vary the size of the moving window depending on the normal change of the process. Then the VMWKPCA method is performed for monitoring a Chemical reactor (CSTR). The simulation results proved that the new method is effective.

Original languageEnglish
Title of host publication2017 International Conference on Control, Automation and Diagnosis, ICCAD 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages236-241
Number of pages6
ISBN (Electronic)9781509059874
DOIs
Publication statusPublished - 19 Oct 2017
Externally publishedYes
Event2017 International Conference on Control, Automation and Diagnosis, ICCAD 2017 - Hammamet, Tunisia
Duration: 19 Jan 201721 Jan 2017

Other

Other2017 International Conference on Control, Automation and Diagnosis, ICCAD 2017
CountryTunisia
CityHammamet
Period19/1/1721/1/17

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Keywords

  • fault detection
  • Kernel principal component analysis
  • MWKPCA
  • Principal Component Analysis
  • SPE
  • T
  • VMWKPCA

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

Cite this

Fezai, R., Jaffel, I., Taouali, O., Harkat, M-F., & Bouguila, N. (2017). Online process monitoring based on kernel method. In 2017 International Conference on Control, Automation and Diagnosis, ICCAD 2017 (pp. 236-241). [8075663] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CADIAG.2017.8075663