Moving window KPCA with reduced complexity for nonlinear dynamic process monitoring

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

Research output: Contribution to journalArticle

33 Citations (Scopus)


This paper proposes an improved Reduced Kernel Principal Component Analysis (RKPCA) for handling nonlinear dynamic systems. The proposed method is entitled Moving Window Reduced Kernel Principal Component Analysis (MW-RKPCA). It consists firstly in approximating the principal components (PCs) of the KPCA model by a reduced data set that approaches “properly” the system behavior in the order to elaborate an RKPCA model. Secondly, the proposed MW-RKPCA consists on updating the RKPCA model using a moving window. The relevance of the proposed MW-RKPCA technique is illustrated on a Tennessee Eastman process.

Original languageEnglish
Pages (from-to)184-192
Number of pages9
JournalISA Transactions
Publication statusPublished - 1 Sep 2016
Externally publishedYes



  • Fault detection
  • KPCA
  • Nonlinear dynamic process

ASJC Scopus subject areas

  • Instrumentation
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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