An improved PCA scheme for sensor FDI

Application to an air quality monitoring network

Mohamed-Faouzi Harkat, Gilles Mourot, José Ragot

Research output: Contribution to journalArticle

106 Citations (Scopus)

Abstract

In this paper a sensor fault detection and isolation procedure based on principal component analysis (PCA) is proposed to monitor an air quality monitoring network. The PCA model of the network is optimal with respect to a reconstruction error criterion. The sensor fault detection is carried out in various residual subspaces using a new detection index. For our application, this index improves the performance compared to classical detection index SPE. The reconstruction approach allows, on one hand, to isolate the faulty sensors and, on the other hand, to estimate the fault amplitudes.

Original languageEnglish
Pages (from-to)625-634
Number of pages10
JournalJournal of Process Control
Volume16
Issue number6
DOIs
Publication statusPublished - 1 Jul 2006
Externally publishedYes

Fingerprint

Network Monitoring
Air Quality
Air quality
Principal component analysis
Principal Component Analysis
Fault detection
Sensor
Monitoring
Sensors
Fault Detection and Isolation
Fault Detection
Monitor
Fault
Subspace
Estimate
Model

Keywords

  • Air quality monitoring network
  • Fault diagnosis
  • Principal component analysis
  • Sensor failure detection and isolation
  • Variable reconstruction

ASJC Scopus subject areas

  • Process Chemistry and Technology
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

An improved PCA scheme for sensor FDI : Application to an air quality monitoring network. / Harkat, Mohamed-Faouzi; Mourot, Gilles; Ragot, José.

In: Journal of Process Control, Vol. 16, No. 6, 01.07.2006, p. 625-634.

Research output: Contribution to journalArticle

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