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


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Article Info

DATE

2019-03-16

AUTHORS

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

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). More... »

PAGES

1-11

References to SciGraph publications

  • 2018-10. Decentralized fault detection and isolation using bond graph and PCA methods in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2017-07. A new fault detection index based on Mahalanobis distance and kernel method in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2018-12. Online fault detection and isolation of an AIR quality monitoring network based on machine learning and metaheuristic methods in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2019-05. A new Bio-CAD system based on the optimized KPCA for relevant feature selection in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2017-02. Kernel principal component analysis with reduced complexity for nonlinear dynamic process monitoring in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2019-02-01. Supervised process monitoring and fault diagnosis based on machine learning methods in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2018-06. Nonlinear process monitoring based on new reduced Rank-KPCA method in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • 2015-09. Online process monitoring using a new PCMD index in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 1986-01. Methodology for designing air quality monitoring networks: I. Theoretical aspects in ENVIRONMENTAL MONITORING AND ASSESSMENT
  • 2016-07. New fault detection method based on reduced kernel principal component analysis (RKPCA) in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
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    http://scigraph.springernature.com/pub.10.1007/s00170-019-03520-9

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