Multiple sensor fault detection and isolation of an air quality monitoring network using RBF-NLPCA model

M. F. Harkat, G. Mourot, J. Ragot

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

This paper presents a data-driven method based on nonlinear principal component analysis to detect and isolate multiple sensor faults. The RBF-NLPCA model is obtained by combining a principal curve algorithm and two three layer radial basis function (RBF) networks. The reconstruction approach for multiple sensors is proposed in the non linear case and successfully applied for multiple sensor fault detection and isolation of an air quality monitoring network. The proposed approach reduces considerably the number of reconstruction combinations and allows to determine replacement values for the faulty sensors.

Original languageEnglish
Title of host publicationSAFEPROCESS'09 - 7th IFAC International Symposium on Fault Detection, Supervision and Safety of Technical Systems, Proceedings
Pages828-833
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2009
Event7th IFAC International Symposium on Fault Detection, Supervision and Safety of Technical Systems, SAFEPROCESS'09 - Barcelona, Spain
Duration: 30 Jun 20093 Jul 2009

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
ISSN (Print)1474-6670

Other

Other7th IFAC International Symposium on Fault Detection, Supervision and Safety of Technical Systems, SAFEPROCESS'09
CountrySpain
CityBarcelona
Period30/6/093/7/09

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ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Harkat, M. F., Mourot, G., & Ragot, J. (2009). Multiple sensor fault detection and isolation of an air quality monitoring network using RBF-NLPCA model. In SAFEPROCESS'09 - 7th IFAC International Symposium on Fault Detection, Supervision and Safety of Technical Systems, Proceedings (pp. 828-833). (IFAC Proceedings Volumes (IFAC-PapersOnline)). https://doi.org/10.3182/20090630-4-ES-2003.0305