Comparison of Univariate Control Charts with Transformation in Variability Measure
DOI:
https://doi.org/10.18041/1909-2458/ingeniare.13.623Keywords:
Measure of variability, Transformation, Sensitivity, Simulation R, ARL and Alarm signalsAbstract
Monitoring of the variability of a quality feature using univariate control charts in a production processiscritical to detect signifi cant changes in order to allow the engineer to control the condition and behavior of thatfeature. Alwan [1] proposes the study to improve the sensitivity in univariate control charts by transforming the measure of variability transformation taking the standard deviation for the logarithmic EWMA chartsproposed by Crowder [2], fi nding an improvement in sensitivity to small changes in variability. This sensitivity improves in HV CUSUM proposal which applies a transformation to the standard deviation of three parameters [3] fi rst proposed by Castagliola [3].
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References
[1] L. Alwan, Statistical process analysis, McGraw-Hill, 2000.
[2] S. W. Crowder y M. Hamilton, “An ewma for monitoring a process standard deviation”, Journal of Quality, n°. 24, 1992.
[3] P. A. Castagliola, “New ewma control chart for monitoring the process variance”, Quality and Reliability Engineering International, n°. 1, pp. 781-794, 2005.
[4] J. A. Vargas, Control Estadístico de Calidad, Unibiblos, 2007.
[5] R. J. Herrera, Seis Sigma Métodos Estadísticos y sus Aplicaciones, Ecoe Ediciones, 2006.
[6] D. Montgomery, Introduction to Statistical Quality Control, 2001.
[7] F. Bacelli, G. Cohen, G.J. Olsder, J.P. Quadrat, C. Acosta, J. Mejia, J. Pignatello, V. Reo. “A comparation of control charting procedures for monitoring process dispersión”, IIE Transactions, n°. 31, pp. 569-579, 1999.
[8] D. Hawkins, A cusum for a scale parameter, Wiley, 1981.
[9] T. Chang, y F. Gan, “Accumulative sum control chart for monitoring process variance”, Journal of Quality Technology, n°. 27, pp. 109-119, 1995.
[10] J. F. Lawless, Statistical models and methods for lifetime data, John Wiley & Sons, 1982.
[11] N.L. Johson, N. Balakrishnan, “Continuous univariate distributions”, Journal of Quality Technology, n°. 13, pp. 228-231, 1994.
[2] S. W. Crowder y M. Hamilton, “An ewma for monitoring a process standard deviation”, Journal of Quality, n°. 24, 1992.
[3] P. A. Castagliola, “New ewma control chart for monitoring the process variance”, Quality and Reliability Engineering International, n°. 1, pp. 781-794, 2005.
[4] J. A. Vargas, Control Estadístico de Calidad, Unibiblos, 2007.
[5] R. J. Herrera, Seis Sigma Métodos Estadísticos y sus Aplicaciones, Ecoe Ediciones, 2006.
[6] D. Montgomery, Introduction to Statistical Quality Control, 2001.
[7] F. Bacelli, G. Cohen, G.J. Olsder, J.P. Quadrat, C. Acosta, J. Mejia, J. Pignatello, V. Reo. “A comparation of control charting procedures for monitoring process dispersión”, IIE Transactions, n°. 31, pp. 569-579, 1999.
[8] D. Hawkins, A cusum for a scale parameter, Wiley, 1981.
[9] T. Chang, y F. Gan, “Accumulative sum control chart for monitoring process variance”, Journal of Quality Technology, n°. 27, pp. 109-119, 1995.
[10] J. F. Lawless, Statistical models and methods for lifetime data, John Wiley & Sons, 1982.
[11] N.L. Johson, N. Balakrishnan, “Continuous univariate distributions”, Journal of Quality Technology, n°. 13, pp. 228-231, 1994.
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Published
2017-08-25
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Section
Articulo de reflexión
How to Cite
1.
Herrera Acosta RJ, Mendoza Mendoza AA, Fontalvo Herrera T. Comparison of Univariate Control Charts with Transformation in Variability Measure. ingeniare [Internet]. 2017 Aug. 25 [cited 2025 Dec. 5];(13):51-7. Available from: https://revistas.unilibre.edu.co/index.php/ingeniare/article/view/623
