Online reduced kernel principal component analysis for process monitoring

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

Research output: Contribution to journalArticle

31 Citations (Scopus)


Kernel principal component analysis (KPCA), which is a nonlinear extension of principal component analysis (PCA), has gained significant attention as a monitoring method for nonlinear processes. However, KPCA cannot perform well for dynamic systems and when the training data set is large. Therefore, in this paper, an online reduced KPCA algorithm for process monitoring is proposed. The process monitoring performances are studied using two examples: a numerical example and Tennessee Eastman Process (TEP). The simulation results demonstrate the effectiveness of the proposed method when compared to the online KPCA method.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalJournal of Process Control
Publication statusPublished - 1 Jan 2018



  • Dictionary
  • Dynamic process
  • Fault detection
  • Kernel PCA
  • Principal component analysis (PCA)
  • Reduced kernel PCA

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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