A new fault detection method for nonlinear process monitoring

Radhia Fazai, Okba Taouali, Mohamed-Faouzi Harkat, Nasereddine Bouguila

Research output: Contribution to journalArticle

26 Citations (Scopus)


Kernel Principal Component Analysis (KPCA) is a nonlinear extension of Principal Component Analysis (PCA). Recently, it is the most popular technique for monitoring nonlinear processes. However, the time-varying property of the industrial processes requires the adaptive ability of the KPCA. Therefore, in this paper, a Variable Moving Window Kernel PCA (VMWKPCA) method is proposed to update the KPCA model. The concept of this method consists of varying the size of the moving window according to the change of the normal process. To evaluate the performance of the proposed method, the VMWKPCA is applied for monitoring a Continuous Stirred Tank Reactor (CSTR) and a Tennessee Eastman process (TE). The results are satisfactory compared to the conventional Moving Window Kernel PCA (MWKPCA) and the Adaptive Kernel PCA (AKPCA).

Original languageEnglish
Pages (from-to)3425-3436
Number of pages12
JournalInternational Journal of Advanced Manufacturing Technology
Issue number9-12
Publication statusPublished - 1 Dec 2016
Externally publishedYes



  • Fault detection
  • KPCA
  • Variable Moving Window Kernel PCA (VMWKPCA)

ASJC Scopus subject areas

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

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