Fault detection of an air quality monitoring network

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

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

3 Citations (Scopus)

Abstract

Concerns for the environment, health and safety are of major importance and have been attracting considerable attention around the globe due to the new environmental challenges that are threatening our planet. In this paper, we propose to enhance the fault detection of an air quality monitoring network (AQMN) by using wavelet principal component analysis (WPCA)-based on generalized likelihood ratio test (GLRT). The presence of measurement noise in the data and model uncertainties degrade the quality of fault detection (FD) techniques by increasing the rate of false alarms. Therefore, the objective of this paper is to enhance the FD of an AQMN by using wavelet representation of data, which is a powerful feature extraction tool to remove the noises from the data. Wavelet data representation has been used to enhance the FD abilities of principal component analysis. Therefore, in the current work, we propose to use WPCA-based on GLRT technique for FD. The fault detection performances of the WPCA-based GLRT technique are shown using air quality monitoring network (AQMN). The results showed the detection efficiency of developed WPCA-based GLRT technique, when compared to classical PCA and WPCA techniques.

Original languageEnglish
Title of host publication2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages229-233
Number of pages5
ISBN (Electronic)9781509034079
DOIs
Publication statusPublished - 16 Jun 2017
Event17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Sousse, Tunisia
Duration: 19 Dec 201621 Dec 2016

Other

Other17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016
CountryTunisia
CitySousse
Period19/12/1621/12/16

    Fingerprint

Keywords

  • Air Quality Monitoring Network
  • Generalized Likelihood Ratio Test
  • Wavelet Principle Component Analysis

ASJC Scopus subject areas

  • Hardware and Architecture
  • Automotive Engineering
  • Control and Optimization
  • Instrumentation
  • Artificial Intelligence

Cite this

Baklouti, R., Ben Hamida, A., Mansouri, M., & Nounou, M. (2017). Fault detection of an air quality monitoring network. In 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings (pp. 229-233). [7952051] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/STA.2016.7952051