Fault Detection of Biological Phenomena Modeled by S-systems

Majdi Mansouri, Mohamed Faouzi Harkat, Hazem Nounou, Mohamed Nounou

Research output: Contribution to journalArticle

Abstract

In this work we propose a novel fault detection (FD) technique in order to enhance monitoring of biological processes. To do that, a new statistical FD method, that is based on combining the advantages of the double exponentially weighted moving average (EWMA), called Max-DEWMA, with those of the particle filtering (PF), and multiscale representation is developed. The advantages of PF-based multiscale (MS) Max-DEWMA (M-DEWMA) are threefold: (i) the dynamical multiscale representation is proposed to extract accurate deterministic features and decorrelate autocorrelated measurements; (ii) PF is proposed to estimate the states of biological processes; (iii) MS-M-DEWMA chart is able to detect smaller fault shifts in the mean/variances and enhance the monitoring of biological processes. The FD performance is studied using Cad System in E. coli (CSEC) model. PF-based MS-M-DEWMA is used to enhance FD of the CSEC model through monitoring some of the key variables involved in this model such as enzymes, lysine and cadaverine.

Original languageEnglish
Pages (from-to)1305-1310
Number of pages6
JournalIFAC-PapersOnLine
Volume51
Issue number24
DOIs
Publication statusPublished - 1 Jan 2018

Fingerprint

Fault detection
Escherichia coli
Monitoring
Enzymes

Keywords

  • Cad System in E. coli
  • Exponentially weighted moving average
  • fault detection
  • Max-Double
  • particle filtering

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Fault Detection of Biological Phenomena Modeled by S-systems . / Mansouri, Majdi; Harkat, Mohamed Faouzi; Nounou, Hazem; Nounou, Mohamed.

In: IFAC-PapersOnLine, Vol. 51, No. 24, 01.01.2018, p. 1305-1310.

Research output: Contribution to journalArticle

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