Kernel PCA-based GLRT for nonlinear fault detection of chemical processes

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40 Citations (Scopus)

Abstract

Fault detection is often utilized for proper operation of chemical processes. In this paper, a nonlinear statistical fault detection using kernel principal component analysis (KPCA)-based generalized likelihood ratio test (GLRT) is proposed. The objective of this work is to extend our previous work (Harrou et al. (2013) to achieve further improvements and widen the applicability of the developed method in practice by using the KPCA method. The KPCA presented here is derived from the nonlinear case of principal component analysis (PCA) algorithm and it is investigated here as modeling algorithm in the task of fault detection. The fault detection problem is addressed so that the data are first modeled using the KPCA algorithm and then the faults are detected using GLRT. The detection stage is related to the evaluation of detection indices, which are signals that reveal the fault presence. Those indices are obtained from the analysis of the difference between the process measurements and their estimations using the KPCA technique. The fault detection performance is illustrated through two simulated examples, one using synthetic data and the other using simulated continuously stirred tank reactor (CSTR) data. The results demonstrate the effectiveness of the KPCA-based GLRT method over the conventional KPCA method through its two charts T2 and Q for detection of single as well as multiple sensor faults.

Original languageEnglish
Pages (from-to)334-347
Number of pages14
JournalJournal of Loss Prevention in the Process Industries
Volume40
DOIs
Publication statusPublished - 1 Mar 2016

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Keywords

  • CSTR process
  • Generalized likelihood ratio test
  • Kernel principal component analysis
  • Nonlinear fault detection

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Food Science
  • Chemical Engineering(all)
  • Safety, Risk, Reliability and Quality
  • Energy Engineering and Power Technology
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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