Fault detection of nonlinear systems using an improved KPCA method

M. Ziyan Sheriff, M. Nazmul Karim, Mohamed Nounou, Hazem Nounou, Majdi Mansouri

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

2 Citations (Scopus)

Abstract

Statistical control charts are essential to ensure both safety and efficient operation of many industrial processes. Many dimensionality reduction techniques such as principal component analysis (PCA) and Partial Least Squares (PLS) regression exist, and are often employed for modeling purposes as they are relatively easy to compute. However, these techniques are only effective for modeling and monitoring linear processes. The Kernel Principal Component Analysis (KPCA) method is an extension of PCA that helps deal with any nonlinearities in the process data. However, KPCA-based fault detection methods may result in a higher false alarm rate than the conventional method. In this paper, an improved KPCA method is developed in order to tackle the issue of high false alarm rates, by utilizing a mean filter to smoothen the detection statistics that are obtained from the KPCA method. The advantages presented by the developed method are illustrated using a simulated nonlinear model. The results clearly show that the improved KPCA method provides improved fault detection results with low missed detection and false alarm rates, and smaller ARL1 values compared to the conventional methods.

Original languageEnglish
Title of host publication2017 4th International Conference on Control, Decision and Information Technologies, CoDIT 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages36-41
Number of pages6
Volume2017-January
ISBN (Electronic)9781509064656
DOIs
Publication statusPublished - 8 Nov 2017
Event4th International Conference on Control, Decision and Information Technologies, CoDIT 2017 - Barcelona, Spain
Duration: 5 Apr 20177 Apr 2017

Other

Other4th International Conference on Control, Decision and Information Technologies, CoDIT 2017
CountrySpain
CityBarcelona
Period5/4/177/4/17

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Keywords

  • Fault detection
  • Kernel principal component analysis
  • Process monitoring

ASJC Scopus subject areas

  • Control and Optimization
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Artificial Intelligence

Cite this

Ziyan Sheriff, M., Nazmul Karim, M., Nounou, M., Nounou, H., & Mansouri, M. (2017). Fault detection of nonlinear systems using an improved KPCA method. In 2017 4th International Conference on Control, Decision and Information Technologies, CoDIT 2017 (Vol. 2017-January, pp. 36-41). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CoDIT.2017.8102563