PRINCIPAL COMPONENT CHART FOR MULTIVARIATE STATISTICAL PROCESS CONTROL

Yıl 2011, Cilt: 1 Sayı: 2, 22 - 31, 23.07.2016

Gafar Matanmi Oyeyemı

Öz

Multivariate statistical process control technique (Hotelling T2 chart) was used to monitor four correlated quality characteristics (active detergent, moisture content, bulk density and ph level) of detergent produced by a company which indicated out-of-control signal. Principal Component Chart is used as a follow-up to out-of-control signal of the Multivariate Control Chart, to identify the quality characteristic(s) that contributed to the signal. The component scores obtained from the principal component analysis of the four quality characteristics measured were used to identify the quality characteristic(s) that contributed to the out-of-control signaled by the Hotelling T chart. The chart of the first component which accounted for 96.7% of the total variability and has moisture content highly loaded in it is outof-control, which implied that moisture content of the detergent produced by the company is out-of-control

Anahtar Kelimeler

principal components, out-of-control, quality characteristics, control limits, eigen-values

Kaynakça

• Alt, F. B. (1995). Multivariate Quality Control in Encyclopedia of Statistical Sciences 6 New York, John Wiley & Sons.
• Alt, F. B. & Smith, N. D. (1998). Multivariate Process Control. Handbook of Statistics, P. R. Krishnaiah and C. R. Rao (eds). North-Holland Elsevier Science Publishers B. V., 7, 333-351
• Chanda, M. J. (2001). Statistical Quality Control. CRC Press, LLC, 2000 N.W. Corporate Blvd., Boca Raton, Florida 33431
• Hotelling, H. (1947). Multivariate Quality Control. Techniques of Statistical Analysis. McGraw-Hill
• Jackson, J. E. (1991). A User Guide to Principal Components. John Wiley & Sons, N.Y.
• Johnson, R. A. & Wichern, D. W. (2002). Applied Multivariate Statistical Analysis. Prentice Hall.
• Kolarik, W. J. (1999). Creating Quality: Process Design for Results. McGraw-Hill International Edition. Singapore, McGraw-Hill Book Company.
• Runger, G. C. & Alt, F. B. (1996): Choosing principal components for multivariate statistical process control. Communications in Statistics: Theory and Methods, 25, 909-922.
• Runger, G.C. & Montgomery, D.C. (1997) Multivariate and univariate process control: geometry and shift directions. Quality and Reliability Engineering International, 13, 153-158.
• Tracy, N. D., Young, J. C. & Mason, R. L. (1992). Multivariate Control Charts for Individual Observations. Journal of Quality Technology, 24, 88-95.
• Vargas, J. A. (2003). Robust Estimation in Multivariate Control Charts for Individual Observations. Journal of Quality Technology, 35(4), 367-376
• Wieda, S. J. (1994). Multivariate Statistical Process Control- Recent Results and Directions for Future Research, Statistica Neerlandica, 48, 147-168.
• Williams, J. D., Woodall, W. H., Birch, J. B. & Sullivan, J. E. (2006). Distribution of Hotelling´s T2 Statistic Based on the Successive Differences Estimator. Journal of Quality Technology, 38(3), 217-229.
• Woodall, W. H., Spitzner, D. J., Montgomery, D. C. & Gupta, S. (2004). Using Control Charts to Monitor Process and Product Quality Profiles. Journal of Quality Technology, 36, 309-320