Multivariate process monitoring using the dynamic biplot

International Statistical Review

Volumn 65, Issue 3, 1997, Pages 325-349

  Sparks, Ross ^a   Adolphson, Allan ^a   Phatak, Aloke ^a  

^a CSIRO   (Australia)

Author keywords

Gabriel biplot; Multivariate statistical process control; Principal component analysis; Process monitoring

Indexed keywords

EID: 0040515161     PISSN: 03067734     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1751-5823.1997.tb00312.x     Document Type: Article

References (34)

1. Chua, M.-K. & Montgomery, D.C. (1992). Investigation and characterization of a control scheme for multivariate quality control. Quality and Reliability Engineering International, 3, 37-44.
2. Crosier, R.B. (1988). Multivariate generalisation of cumulative sum quality control schemes. Technometrics, 30, 291-304.
3. Doganaksoy, N., Faltin, F.W. & Tucker, W.T. (1991). Identification of out of control quality characteristics in a multivariate manufacturing environment. Communications in Statistics-Theory and Method, 20, 2775-2790.
4. Fuchs, C. & Benjamini, Y. (1994). Multivariate profile charts for statistical process control. Technometrics, 36, 182-195.
5. Gabriel, K.R. (1971). The biplot graphical display of matrices with application to principal component analysis. Biometrika, 58, 453-467.
6. Hawkins, D.M. (1991). Multivariate quality control based on regression-adjusted variables. Technometrics, 33, 61-75.
7. Healy, J.D. (1987). A note on multivariate CUSUM procedures. Technometrics, 29, 409-412.
8. Householder, A.S. & Young, G. (1938). Matrix approximation and latent roots. Am. Math. Monthly, 45, 165-171.
9. Jackson, J.E. (1959). Quality control methods for several related variables. Technometrics, 1, 359-377.
10. m for principal components - What about absolute values? Technometrics, 21, 253-255.
11. Jackson, J.E. & Mudholkar, G.S. (1979). Control procedures for residual procedures associated with principal component analysis. Technometrics, 21, 253-255.
12. Jolliffe, I.T. (1986). Principal Component Analysis. New York: Springer Verlag.
13. Kendall, M. & Stuart, A. (1977). The Advanced Theory of Statistics. Volume I: Distribution Theory. London: Charles Griffin & Co. Ltd.
14. Kresta, J.V., MacGregor, J.F. & Marlin, T.E. (1991). Multivariate statistical monitoring of process operating performance. Canadian Journal of Chemical Engineering, 69, 35-47.
15. Lowry, C.A., Woodall, W.H., Champ, C.W. & Rigdon, S.E. (1992). A multivariate exponentially weighted moving average control chart. Technometrics, 34, 46-53.
16. MacGregor, J.F & Nomikos, P. (1992). Monitoring batch processes. In Batch Processing Systems Engineering: Current Status and Future Directions (NATO ASI Series F), eds. Reklaitis, Rippin, Hortacso and Sunol. Heidelberg: Springer-Verlag.
17. MacGregor, J.F., Jaeckle, C., Kiparissides, C. & Koutoudi, M. (1994). Monitoring and diagnosis of process operating performance by multi-block PLS methods with an application to low density polyethlene production, AIJChE Journal, 40, 826-838.
18. 2 for multivariate control chart interpretation. Journal of Quality Technology, 27, 99-108.
19. Miller, P., Swanson, R.E. & Heckler, C.E. (1996). Contribution plots: The missing link in multivariate quality control. Unpublished manuscript, Eastman Kodak, Rochester, NY.
20. 2 multivariate quality control procedure. The Statistician, 36, 571-583.
21. Nomikos, P. & MacGregor, J.F (1994). Monitoring batch processes using multiway principal component analysis. AIChE Journal, 40, 1361-1375.
22. Nomikos, P. & MacGregor, J.F (1995). Multivariate SPC charts for monitoring batch processes. Technometrics, 37, 41-59.
23. O'Brien, P.C. (1992). Robust procedures for testing equality of covariance matrices. Biometrics, 48, 819-827.
24. Pignatiello, J.J. & Runger, G.C. (1990). Comparisons of multivariate CUSUM charts. Journal of Quality Technology, 22, 173-186.
25. Qtoolbox User's Manual, Version 1.0 (1996). CSIRO Division of Mathematics and Statistics Technical Report DMS-D96/48.
26. Sparks, R.S. (1992). Quality control with multivariate data. Austral. J. Statist., 34, 375-390.
27. Sparks, R.S., Adolphson, A.F. & Phatak, A. (1997). The Dynamic Biplot. Technical Report CMIS-D15/97, CSIRO Mathematical and Information Sciences.
28. Western Electric Corporation(1956). Statistical Quality Control Handbook, Western Electric Corporation, Indianapolis, IN.
29. Whittle, P. (1952). On principal components and least squares methods of factor analysis. Skand. Aktuar., 35, 223-239.
30. Wierda, S.J. (1994). Multivariate statistical process control - recent results and directions for future research. Statistica Neerlandica, 48, 147-168.
31. Wise, B.M., Ricker, N.L., Veltkamp, D.J. & Kowalski, B.R. (1990). A theoretical basis for the use of principal component models for monitoring multivariate processes. Process. Cont. Qual., 1, 41-51.
32. Wise, B.M., Veltkamp, D.J., Ricker, N.L., Kowalski, B.R., Barnes, S.M. & Arakali, V. (1991). Application of multivariate statistical process control to the West Valley Slurry-Red Ceramic Melter Process. Waste Management Proc., Tucson, AZ.
33. Wold, S., Geladi, P., Esbensen, K. & Ohman, J. (1987). Multi-way principal components and PLS Analysis. Journal of Chemometrics, 1, 41-56.
34. Woodall, W.H. & Ncube, M.M. (1985). Multivariate CUSUM quality control procedures. Technometrics, 27, 285-292.