A new monitoring scheme of an air quality network based on the kernel method

Maroua Said, Khaoula ben Abdellafou, Okba Taouali, Mohamed-Faouzi Harkat

Research output: Contribution to journalArticle

Abstract

Air pollution is classified as one of the most dangerous type on the human health, the environment, and the ecosystem. However, air pollution results in climate change and affects people’s health. For a number of years, monitoring the air quality has become a very urgent and necessary topic. Moreover, safety and health have been attracting attention as one of the important topics to evaluate, firstly, the degree of air pollution and predict pollutant concentrations accurately. Then, it is crucial to establish a more scientific air quality monitoring to ensure the quality of life. In this paper, new reduced air quality monitoring is suggested to enhance the Fault Detection (FD) of an air quality monitoring network. Furthermore, a sensor FD procedure based on Reduced Kernel Partial Least Squares (RKPLS) is proposed to monitor an air quality monitoring network. The main contribution of the suggested procedure is to enhance the FD of an air quality monitoring network in terms of computation time and false alarm rate, using just the important latent components, compared to standard Kernel Partial Least Squares (KPLS).

Original languageEnglish
JournalInternational Journal of Advanced Manufacturing Technology
DOIs
Publication statusPublished - 1 Jan 2019

Fingerprint

Air quality
Monitoring
Air pollution
Fault detection
Health
Climate change
Ecosystems
Sensors

Keywords

  • Air pollution
  • Air quality
  • Fault detection
  • KPLS
  • Reduced KPLS
  • SPE

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

Cite this

A new monitoring scheme of an air quality network based on the kernel method. / Said, Maroua; Abdellafou, Khaoula ben; Taouali, Okba; Harkat, Mohamed-Faouzi.

In: International Journal of Advanced Manufacturing Technology, 01.01.2019.

Research output: Contribution to journalArticle

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