Kernel principal component analysis with reduced complexity for nonlinear dynamic process monitoring

Ines Jaffel, Okba Taouali, Mohamed-Faouzi Harkat, Hassani Messaoud

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

This paper proposes a new reduced kernel method for monitoring nonlinear dynamic systems on reproducing kernel Hilbert space (RKHS). Here, the proposed method is a concatenation of two techniques proposed in our previous studies, the reduced kernel principal component (RKPCA) Taouali et al. (Int J Adv Manuf Technol, 2015) and the singular value decomposition-kernel principal component (SVD-KPCA) (Elaissi et al. (ISA Trans, 52(1), 96–104, 2013)) The proposed method is entitled SVD-RKPCA. It consists at first to identify an implicit RKPCA model, that approaches “properly” the system behavior, and after that to update this RKPCA model by SVD of an incremented and decremented kernel matrix using a moving data window. The proposed SVD-RKPCA has been applied successfully for monitoring of a continuous stirred tank reactor (CSTR) as well as a Tennessee Eastman process (TEP).

Original languageEnglish
Pages (from-to)3265-3279
Number of pages15
JournalInternational Journal of Advanced Manufacturing Technology
Volume88
Issue number9-12
DOIs
Publication statusPublished - 1 Feb 2017
Externally publishedYes

Fingerprint

Process monitoring
Singular value decomposition
Principal component analysis
Monitoring
Hilbert spaces
Dynamical systems

Keywords

  • Fault detection
  • Fault isolation
  • KPCA
  • Process monitoring
  • RKPCA
  • SVD

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

Cite this

Kernel principal component analysis with reduced complexity for nonlinear dynamic process monitoring. / Jaffel, Ines; Taouali, Okba; Harkat, Mohamed-Faouzi; Messaoud, Hassani.

In: International Journal of Advanced Manufacturing Technology, Vol. 88, No. 9-12, 01.02.2017, p. 3265-3279.

Research output: Contribution to journalArticle

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