Diagnosis of nonlinear systems using reduced kernel principal component analysis

Radhia Fezai, Kamaleldin Abodayeh, Majdi Mansouri, Hazem Nounou, Mohamed Nounou

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

Abstract

In this article, we consider a sensor fault detection and identification procedure based on reduced kernel principal component analysis (KPCA). The fault detection based on reduced KPCA method consists first to reduce the amount of training data using K-means clustering in the input space while conserving the structure of the data in the feature space. Then, it consists to built KPCA model and use it for fault detection. The proposed fault identification based on reduced KPCA uses reconstruction-based contributions to identify and estimate the fault using the reduced KPCA model. The proposed fault detection and identification methods are tested with a simulated CSTR process. The simulation results show that the proposed fault detection and identification methods are effective for KPCA.

Original languageEnglish
Title of host publication2019 International Conference on Control, Automation and Diagnosis, ICCAD 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728122922
DOIs
Publication statusPublished - Jul 2019
Event3rd International Conference on Control, Automation and Diagnosis, ICCAD 2019 - Grenoble, France
Duration: 2 Jul 20194 Jul 2019

Publication series

Name2019 International Conference on Control, Automation and Diagnosis, ICCAD 2019 - Proceedings

Conference

Conference3rd International Conference on Control, Automation and Diagnosis, ICCAD 2019
CountryFrance
CityGrenoble
Period2/7/194/7/19

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Keywords

  • CSTR
  • fault detection
  • fault estimation
  • fault identification
  • K-means
  • KPCA
  • Nonlinear process
  • RBC-KPCA

ASJC Scopus subject areas

  • Artificial Intelligence
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

Cite this

Fezai, R., Abodayeh, K., Mansouri, M., Nounou, H., & Nounou, M. (2019). Diagnosis of nonlinear systems using reduced kernel principal component analysis. In 2019 International Conference on Control, Automation and Diagnosis, ICCAD 2019 - Proceedings [9037905] (2019 International Conference on Control, Automation and Diagnosis, ICCAD 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCAD46983.2019.9037905