Fault diagnosis for dynamic nonlinear system based on variable moving window KPCA

Radhia Fezai, Majdi Mansouri, Okba Taouali, Mohamed-Faouzi Harkat, Nasreddine Bouguila

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

Abstract

Kernel Principal Component Analysis (KPCA) is a noteworthy nonlinear extension of the most popular dimensionality reduction methods, Principal Component Analysis (PCA). It has been extensively used for process monitoring. The time varying property of industrial processs require the adaptive ability of KPCA. The Variable Moving Window KPCA (VMWKPCA) is developed to monitor the dynamic processes. This new method is based on the variation of the size of the moving window depending on the normal change of the system. For fault diagnosis a set of structured partial VMWKPCA were used. The fault detection and diagnosis with the proposed VMWKPCA are tested using the Continuous Stirred Tank Reactor (CSTR] process. The simulation results proved that the new method is effective for fault detection and diagnosis,

Original languageEnglish
Title of host publication2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages590-595
Number of pages6
ISBN (Electronic)9781538653050
DOIs
Publication statusPublished - 7 Dec 2018
Event15th International Multi-Conference on Systems, Signals and Devices, SSD 2018 - Yassmine, Hammamet, Tunisia
Duration: 19 Mar 201822 Mar 2018

Other

Other15th International Multi-Conference on Systems, Signals and Devices, SSD 2018
CountryTunisia
CityYassmine, Hammamet
Period19/3/1822/3/18

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Keywords

  • dynamic process
  • fault detection
  • fault diagnosis
  • Kernel PCA
  • Principal component analysis
  • structured partial VMWKPCA
  • Variable Moving Window KPCA

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Control and Optimization
  • Instrumentation

Cite this

Fezai, R., Mansouri, M., Taouali, O., Harkat, M-F., & Bouguila, N. (2018). Fault diagnosis for dynamic nonlinear system based on variable moving window KPCA. In 2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018 (pp. 590-595). [8570630] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSD.2018.8570630