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Industrial Production of Gypsum: Quality Control Charts

  • Luís M. GriloEmail author
  • Helena L. Grilo
  • Cristiano J. Marques
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 136)

Abstract

The production of gypsum (marketed if it accomplishes the required specifications) occurs during the process of flue gas desulphurization in a Portuguese Coal Thermoelectric Central. Important variables in this process are statistically analyzed in the chemical laboratory and quality control charts are implemented to monitor the entire process. In this study individuals and moving range charts of the variable “density of gypsum slurry” are compared with the “more efficient” ones obtained after a Box-Cox transformation. This transformation is used to normalize the data, because its observations come from non-normal models—where classical control charts are considered less appropriate, since they usually exhibit rates of false alarms different from what would be expected. Although it is important to consider different statistical approaches for quality control charts, during the monitoring of an industrial process, in this case study the achieved results lead us, essentially, to similar conclusions.

Keywords

Non-normality Box-Cox transformation Individuals and moving range charts Robustness 

References

  1. 1.
    Borror, C.M., Montgomery, D.C., Runger, G.C.: Robustness of the EWMA control chart to non-normality. J. Qual. Technol. 31(3), 309–316 (1999)Google Scholar
  2. 2.
    Box, G.E.P., Cox, D.R.: An analysis of transformations. J. R. Stat. Soc. 26, 211–252 (1964)zbMATHMathSciNetGoogle Scholar
  3. 3.
    Figueiredo, F., Gomes, M.I.: Estimadores robustos de localização. A Estatística em Movimento. In: Neves, M.M., Cadima, J., Martins e, M.J., Rosado, F. (eds.) Sociedade Portuguesa de Estatística, pp. 193–204 (2001)Google Scholar
  4. 4.
    Figueiredo, F., Gomes, M.I.: Transformação de dados em controlo estatístico de qualidade. Novos Rumos em Estatística. In: Carvalho, L., Brilhante e, F., Rosado, F. (eds.) Sociedade Portuguesa de Estatística, pp. 235–245 (2002)Google Scholar
  5. 5.
    Figueiredo, F., Gomes, M.I.: Monitoring industrial processes with robust control charts. Revstat Stat. J. 7, 151–170 (2009)MathSciNetGoogle Scholar
  6. 6.
    Montgomery, D.C.: Introduction to Statistical Quality Control, 4th edn. Wiley, New York (2000)Google Scholar
  7. 7.
    Stoumbos, Z.G., Reynolds Jr, M.R.: Robustness to no-normality and autocorrelation of individuals control charts. J. Stat. Comput. Simul. 66, 145–187 (2000)CrossRefzbMATHGoogle Scholar
  8. 8.
    Tatum, L.G.: Robust estimation of the process standard deviation for control charts. Technometrics 39(2), 127–141 (1997)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Wheeler, D.J.: When can we trust the limits on a process behavior chart? Qual. Dig. (2009)Google Scholar
  10. 10.
    Wheeler D.J.: Good limits from bad data. Qual. Dig. (2009)Google Scholar
  11. 11.
    Wheeler D.J.: Do you have Leptokurtophobia? Qual. Dig. (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Luís M. Grilo
    • 1
    Email author
  • Helena L. Grilo
    • 2
  • Cristiano J. Marques
    • 3
  1. 1.Unidade Departamental de Matemática e FísicaInstituto Politécnico de TomarTomarPortugal
  2. 2.Centro de Sondagens e Estudos EstatísticosInstituto Politécnico de TomarTomarPortugal
  3. 3.Unidade Departamental de EngenhariaInstituto Politécnico de TomarTomarPortugal

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