Recursive kernel PCA-based GLRT for fault detection: Application to an air quality monitoring network

Raoudha Baklouti, Majdi Mansouri, Hazem Nounou, Mohamed Nounou, Ahmed Ben Hamida

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

1 Citation (Scopus)

Abstract

This paper aims to improve the use of generalized likelihood ratio test (GLRT) method for fault detection. To achieve this objective, nonlinear fault detection method will be developed. Kernel principal component analysis (kPCA) models have been widely used to represent nonlinear systems. KPCA models rely of transforming the data in a linear form to a higher dimensional spacee. Unfortunately, kPCA models are batch, i.e., they require the availability of the process data before constructing the model. In most situations, however, fault detection is needed online, i.e., as the data are collected from the process. Therefore, recursive kPCA fault detection technique will be developed in order to extend the advantages of the GLRT to online processes. The fault detection performances of the recursive kPCA-based GLRT technique are shown using air quality monitoring network (AQMN). The results showed the effectiveness of the developed algorithm over conventional method.

Original languageEnglish
Title of host publication2017 International Conference on Smart, Monitored and Controlled Cities, SM2C 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages152-155
Number of pages4
ISBN (Electronic)9781509063239
DOIs
Publication statusPublished - 18 Oct 2017
Event2017 International Conference on Smart, Monitored and Controlled Cities, SM2C 2017 - Kerkennah-Sfax, Tunisia
Duration: 17 Feb 201719 Feb 2017

Other

Other2017 International Conference on Smart, Monitored and Controlled Cities, SM2C 2017
CountryTunisia
CityKerkennah-Sfax
Period17/2/1719/2/17

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Keywords

  • Air Quality Monitoring Network
  • GLRT
  • Recursive kernel PCA

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Health(social science)
  • Urban Studies

Cite this

Baklouti, R., Mansouri, M., Nounou, H., Nounou, M., & Hamida, A. B. (2017). Recursive kernel PCA-based GLRT for fault detection: Application to an air quality monitoring network. In 2017 International Conference on Smart, Monitored and Controlled Cities, SM2C 2017 (pp. 152-155). [8071839] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SM2C.2017.8071839