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

  • Maroua Said
  • Khaoula ben Abdellafou
  • Okba TaoualiEmail author
  • Mohamed Faouzi Harkat


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).


Air pollution Air quality KPLS Reduced KPLS SPE Fault detection 


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  1. 1.
    Harkat MF, Mourot G, Gilles R (2006) An improved PCA scheme for sensor FDI: application to an air quality monitoring network. J Process Control 16(6):625–634CrossRefGoogle Scholar
  2. 2.
    Mofarrah A, Husain T (2010) A holistic approach for optimal design of air quality monitoring network expansion in an urban area. Atmos Environ 44(3):432–440CrossRefGoogle Scholar
  3. 3.
    Yang Z, Wang J (2017) A new air quality monitoring and early warning system: air quality assessment and air pollutant concentration prediction. Environ Res 158:105–117CrossRefGoogle Scholar
  4. 4.
    Liu MK, Avrin J, Pollack R, Behar J, McElroy J (1986) Methodology for designing air quality monitoring networks: I. theoretical aspects. Environ Monit Assess 6(1):1–11CrossRefGoogle Scholar
  5. 5.
    Stanimirova I, Simeonov V (2005) Modeling of environmental four-way data from air quality control. Chemom Intell Lab Syst 77(1-2):115–121CrossRefGoogle Scholar
  6. 6.
    Zheng J, Zhong L, Wang T, Louie P, Li Z (2010) Ground-level ozone in the pearl river delta region: analysis of data from a recently established regional air quality monitoring network. Atmos Environ 44(6):814–823CrossRefGoogle Scholar
  7. 7.
    Jaffel I, Taouali O, Harkat MF, Messaoud H (2015) Online process monitoring using a new PCMD index. Int J Adv Manuf Technol 80(5-8):947–957CrossRefGoogle Scholar
  8. 8.
    Willsky A, Chow E, Gershwin S, Greene C, Houpt P, Kurkjian A (1980) Dynamic model-based techniques for the detection of incidents on freeways. IEEE Trans Autom Control 25(3):347–360CrossRefzbMATHGoogle Scholar
  9. 9.
    Venkatasubramanian V, Rengaswamy R, Gershwin S, Kavuri S, Yin K (2003) A review of process fault detection and diagnosis: Part III: process history based methods. Comput Chem Eng 27(3):327–346CrossRefGoogle Scholar
  10. 10.
    Yan S, Huang J, Yan X, Kavuri S, Yin K (2003) Monitoring of quality-relevant and quality-irrelevant blocks with characteristic-similar variables based on self-organizing map and kernel approaches. J Process Control 73:103–112CrossRefGoogle Scholar
  11. 11.
    Benothman K, Maquin D, Ragot R, Benrejeb M (2007) Diagnosis of uncertain linear systems: an interval approach. Int J Sci Tech Automatic Control Comput Eng 1(2):136–154Google Scholar
  12. 12.
    Lahdhiri H, Taouali O, Elaissi I, Jaffel I, Harakat MF, Messaoud H (2017) A new fault detection index based on Mahalanobis distance and kernel method. Int J Adv Manuf Technol 91(5-8):2799–2809CrossRefGoogle Scholar
  13. 13.
    Joe Qin S (2003) Statistical process monitoring: basics and beyond. J Chemom 17(8-9):480–502CrossRefGoogle Scholar
  14. 14.
    Jaffel I, Taouali O, Elaissi I, Jaffel I, Messaoud H (2013) A new online fault detection method based on PCA technique. IMA J Math Control Inf 31(4):487–499MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Said M, Fazai R, Abdellafou K, Taouali O (2018) Decentralized fault detection and isolation using bond graph and PCA methods. Int J Adv Manuf Technol 99(1-4):517–529CrossRefGoogle Scholar
  16. 16.
    Kano M, Tanaka S, Hasebe S, Hashimoto I, Ohno H (2003) Monitoring independent components for fault detection. AIChE J 49(4):969–976CrossRefGoogle Scholar
  17. 17.
    Li G, Qin S, Zhou D, Hashimoto I, Ohno H (2003) Geometric properties of partial least squares for process monitoring. Automatica 46(1):204–210MathSciNetCrossRefGoogle Scholar
  18. 18.
    Wold H (1985) Partial least squares. Encyclopedia of statistical sciencesGoogle Scholar
  19. 19.
    Neffati S, Abdellafou K, Taouali O, Bouzrara K (2019) A new bio-CAD system based on the optimized KPCA for relevant feature selection. Int J Adv Manuf Technol: 1–12.
  20. 20.
    Harkat MF, Mansouri M, Nounou M, Nounou H (2018) Enhanced data validation strategy of air quality monitoring network. Environ Res 160:183–194CrossRefGoogle Scholar
  21. 21.
    Tang J, Zhang J, Wu Z, Liu Z, Chai T, Yu W (2017) Modeling collinear data using double-layer GA-based selective ensemble kernel partial least squares algorithm. Automatica 219:248–262Google Scholar
  22. 22.
    MacGregor JF, Jaeckle C, Kiparissides C, Koutoudi M (1994) Process monitoring and diagnosis by multiblock PLS methods. AIChE J 40(5):826–838CrossRefGoogle Scholar
  23. 23.
    Helland K, Berntsen HE, Borgen OS, Martens H (1992) Recursive algorithm for partial least squares regression. Chemom Intell Lab Syst 14(1-3):129–137CrossRefGoogle Scholar
  24. 24.
    Zhou D, Li G, Qin SJ (2010) Total projection to latent structures for process monitoring. AIChE J 56 (1):168–178Google Scholar
  25. 25.
    Rosipal R, Trejo LJ (2001) Kernel partial least squares regression in reproducing kernel hilbert space. J Mach Learn Res 2(Dec):97–123zbMATHGoogle Scholar
  26. 26.
    Zhang Y, Du W, Fan Y, Zhang L (2015) Process fault detection using directional kernel partial least squares. Ind Eng Chem Res 54(9):2509–2518CrossRefGoogle Scholar
  27. 27.
    Zhang Y, Hu Z (2011) Multivariate process monitoring and analysis based on multi-scale KPLS. Chem Eng Res Des 89(12):2667–2678CrossRefGoogle Scholar
  28. 28.
    Kim K, Lee JM, Lee IB (2005) A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction. Chemom Intell Lab Syst 79(1-2):22– 30CrossRefGoogle Scholar
  29. 29.
    Taouali O, Elaissi I, Messaoud H (2015) Dimensionality reduction of RKHS model parameters. ISA Trans 57:205–210CrossRefGoogle Scholar
  30. 30.
    Willis A (2010) Condition monitoring of centrifuge vibrations using kernel PLS. Comput Chem Eng 34 (3):349–353CrossRefGoogle Scholar
  31. 31.
    Wang G, Jiao J, Yin S (2018) Efficient nonlinear fault diagnosis based on kernel sample equivalent replacement. IEEE Trans Ind Inf 3Google Scholar
  32. 32.
    Wang Q (2012) Kernel principal component analysis and its applications in face recognition and active shape models. arXiv:1207.3538
  33. 33.
    Lindgren F, Geladi P, Wold S (1993) The kernel algorithm for PLS. J Chemom 7(1):45–59CrossRefGoogle Scholar
  34. 34.
    Rosipal R, Geladi P, Wold S (2010) Nonlinear partial least squares: an overview. Chemoinformatics and advanced machine learning perspectives: complex computational methods and collaborative techniques: 169–189Google Scholar
  35. 35.
    Jaffel I, Taouali O, Harkat MF, Messaoud H (2017) Kernel principal component analysis with reduced complexity for nonlinear dynamic process monitoring. Int J Adv Manuf Technol 88(9-12):3265–3279CrossRefGoogle Scholar
  36. 36.
    Taouali O, Jaffel I, Lahdhiri H, Harkat MF, Messaoud H (2016) New fault detection method based on reduced kernel principal component analysis (RKPCA). Int J Adv Manuf Technol 85(5-8):1547–1552CrossRefGoogle Scholar
  37. 37.
    Lahdhiri H, Taouali O, Elaissi I, Harkat MF, Messaoud H (2018) Nonlinear process monitoring based on new reduced Rank-KPCA method. Stoch Env Res Risk A 32(6):1833–1848CrossRefGoogle Scholar
  38. 38.
    Lahdhiri H, Said M, Abdellafou K, Taouali O, Harkat MF, Messaoud H (2019) Supervised process monitoring and fault diagnosis based on machine learning methods. Int J Adv Manuf Technol (1–17)Google Scholar
  39. 39.
    Liu X, Kruger U, Elaissi I, Littler T, Xie L, Wang S (2009) Moving window kernel PCA for adaptive monitoring of nonlinear processes. Chemom Intell Lab Syst 96(2):132–143CrossRefGoogle Scholar
  40. 40.
    Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24(6):417CrossRefzbMATHGoogle Scholar
  41. 41.
    Jackson JE, Mudholkar GS (1979) Control procedures for residuals associated with principal component analysis. JTechnometrics 21(3):341–349CrossRefzbMATHGoogle Scholar
  42. 42.
    Lee C, Choi SW, Lee I (2004) Sensor fault identification based on time-lagged PCA in dynamic processes. Chemom Intell Lab Syst 70(2):165–178CrossRefGoogle Scholar
  43. 43.
    Fazai R, Abdellafou K, Said M, Taouali O (2018) Online fault detection and isolation of an AIR quality monitoring network based on machine learning and metaheuristic methods. Int J Adv Manuf Technol: 1–14Google Scholar
  44. 44.
    Bell ML, McDermott A, Zeger SL, Samet JM, Dominici F (2004) Ozone and short-term mortality in 95 US urban communities, 1987-2000. Jama 292(19):2372–2378CrossRefGoogle Scholar
  45. 45.
    Harakat MF, Mourot G, Ragot J (2009) Multiple sensor fault detection and isolation of an air quality monitoring network using RBF-NLPCA model. IFAC Proceedings 42(8):828–833CrossRefGoogle Scholar
  46. 46.
    Zhang T (2001) An introduction to support vector machines and other kernel-based learning methods. AI Mag 22(2):103Google Scholar
  47. 47.
    Qin SJ (2012) Survey on data-driven industrial process monitoring and diagnosis. Annu Rev Control 36 (2):220–234CrossRefGoogle Scholar
  48. 48.
    Jalali-Heravi M, Kyani A (2007) Application of genetic algorithm-kernel partial least square as a novel nonlinear feature selection method: activity of carbonic anhydrase II inhibitors. Eur J Med Chem 42(5):649–659CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Maroua Said
    • 1
  • Khaoula ben Abdellafou
    • 2
  • Okba Taouali
    • 3
    • 4
    Email author
  • Mohamed Faouzi Harkat
    • 5
  1. 1.University of Sousse, National Engineering School of Sousse (ENISO), MARS Research LaboratoryHammam SousseTunisia
  2. 2.Department of Computer Science, Faculty of Computers and Information TechnologyUniversity of TabukTabukSaudi Arabia
  3. 3.Department of Computer Engineering, Faculty of Computers and Information TechnologyUniversity of TabukTabukSaudi Arabia
  4. 4.University of Monastir, National Engineering School of MonastirMonastirTunisia
  5. 5.Department of Electronics, Faculty of Engineering AnnabaBadji MokhtarAnnabaAlgeria

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