Kernel Generalized Likelihood Ratio Test for Fault Detection of Biological Systems

Majdi Mansouri, Raoudha Baklouti, Mohamed-Faouzi Harkat, Mohamed Nounou, Hazem Nounou, Ahmed Ben Hamida

Research output: Contribution to journalArticle

Abstract

In this paper, we develop an improved fault detection (FD) technique in order to enhance monitoring abilities of nonlinear biological processes. Generalized likelihood ratio test (GLRT) based kernel principal component analysis (KPCA) (called also kernel GLRT) is an effective data driven technique for monitoring nonlinear processes. However, it is well known that data collected from complex and multivariate processes are multiscale due to the variety of changes that could occur in process with different localization in time and frequency. Thus, to enhance process monitoring abilities, we propose to combine advantages of kernel GLRT and multiscale representation using wavelets by developing a mutiscale kernel GLRT (MS-KGLRT) detection chart. The proposed fault detection approach is addressed so that the KPCA is used to compute the model in the feature space and the MS-KGLRT chart is applied to detect the faults. The detection performance of the new chart is studied using two examples, one using synthetic data and the other using biological process representing a Cad System in E. coli (CSEC) model for detecting small and moderate shifts (offset or bias and drift). The MS-KGLRT chart is used to enhance fault detection of the CSEC model through monitoring some of the key variables involved in this model such as enzymes, lysine and cadaverine.

Original languageEnglish
JournalIEEE Transactions on Nanobioscience
DOIs
Publication statusAccepted/In press - 1 Jan 2018

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Biological Phenomena
Biological systems
Principal Component Analysis
Fault detection
Cadaverine
Escherichia coli
Principal component analysis
Lysine
Monitoring
Process monitoring
Enzymes

Keywords

  • Biological system modeling
  • Cad System in E. coli (CSEC)
  • Fault detection
  • fault detection (FD)
  • Feature extraction
  • Kernel
  • Kernel generalized likelihood ratio (KGLRT)
  • kernel principal component analysis (KPCA)
  • Monitoring
  • multiscale KGLRT
  • Principal component analysis
  • Solid modeling

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Medicine (miscellaneous)
  • Biomedical Engineering
  • Pharmaceutical Science
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

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abstract = "In this paper, we develop an improved fault detection (FD) technique in order to enhance monitoring abilities of nonlinear biological processes. Generalized likelihood ratio test (GLRT) based kernel principal component analysis (KPCA) (called also kernel GLRT) is an effective data driven technique for monitoring nonlinear processes. However, it is well known that data collected from complex and multivariate processes are multiscale due to the variety of changes that could occur in process with different localization in time and frequency. Thus, to enhance process monitoring abilities, we propose to combine advantages of kernel GLRT and multiscale representation using wavelets by developing a mutiscale kernel GLRT (MS-KGLRT) detection chart. The proposed fault detection approach is addressed so that the KPCA is used to compute the model in the feature space and the MS-KGLRT chart is applied to detect the faults. The detection performance of the new chart is studied using two examples, one using synthetic data and the other using biological process representing a Cad System in E. coli (CSEC) model for detecting small and moderate shifts (offset or bias and drift). The MS-KGLRT chart is used to enhance fault detection of the CSEC model through monitoring some of the key variables involved in this model such as enzymes, lysine and cadaverine.",
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AU - Nounou, Hazem

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