Statistical Process Control for Variance Shift Detections of Multivariate Autocorrelated Processes

References

• Alwan, A. J. and Alwan, L. C. (1994). Monitoring autocorrelated processes using multivariate quality control charts. Proceedings of the Decision Sciences Institute Annual Meeting, 3, 2106–2108.
   Google Scholar
• Alwan, L. C. and Roberts, H. V. (1988). Time series modeling for statistical process control. Journal of Business & Economic Statistics, 6, 87–95.
   Google Scholar
• Alt, B. and Smith, N. D. (1988). Multivariate Process Control. Handbook of Statistics, P.R. Krisnaiah and C. R. Rao, Eds, Elsevier, 7, 333–351.
   Google Scholar
• Apley, D. W. and Tsung, F. (2002). The GLRT for statistical process control of autocorrelated processes. IIE Transactions, 31, 1123–1134.
   Google Scholar
• Atienza, O., Tang, L. C. and Ang, B. W. (1998). A SPC procedure for detecting level shifts of autocorrelated processes. Journal of Quality Technology, 30, 340–351.
   Google Scholar
• Berthouex, P. M., Hunter, W. G., Pallesen, L. and Shih, C. Y. (1976). The use of stochastic models in interpretation of historical data from sewage treatment plants. Water Research, 10, 689–698.
   Google Scholar
• Box, G. E. P., Hunter, W. and Hunter, J. (1978). Statistics for Experimenters. John Wiley, New York, NY.
   Google Scholar
• Bratley, P, Fox, B. L. and Schrang, L. E. (1987). A Guide to Simulation, 2nd edition. Springer-Verlag, NY.
   Google Scholar
• Crosier, R. B. (1988). Multivariate generalizations of cumulative sum quality control schemes. Technometrics, 30, 291–303.
   Google Scholar
• Crowder, S. V. and Hamilton, M., (1992). An EWMA for monitoring a process standard deviation. Journal of Quality Technology, 24, 12–21.
   Google Scholar
• Djauhari, M. (2005). Improved monitoring of multivariate process variability. Journal of Quality Technology, 37, 32–39.
   Google Scholar
• Ermer, D. S. (1980). A control chart for dependent data. ASQC Technical Conference Transactions, Atlanta, GA.
   Google Scholar
• Hawkins, D. (1993). Regression adjustment for variables in multivariate quality control. Journal of Quality Technology, 25, 170–182.
   Google Scholar
• Hotelling, H. (1947). Multivariate Quality Control. Techniques of Statistical Analysis, Eisenhart, Hastay, and Wallis, Eds., McGraw-Hill, NY.
   Google Scholar
• Hu, S. J. (1996). Statistical process control for correlated processes: case studies in automotive manufacturing. International Journal of Modelling and Simulation, 16, 218–223.
   Google Scholar
• Hu, S.J. and Roan, C. (1996). Change patterns of time Series — Based control charts. Journal of Quality Technology, 28, 302–312.
   Google Scholar
• Jiang, W. (2004). Multivariate control charts for monitoring autocorrelated processes. Journal of Quality Technology, 367–379.
   Google Scholar
• Kourti, T. and MacGregor, J. F. (1996). Multivariate SPC methods for process and product monitoring. Journal of Quality Technology, 28, 409–428.
   Google Scholar
• Kramer, H. G. and Schmid, W. (1997). EWMA charts for multivariate time series. Sequential Analysis, 16, 131–154.
   Google Scholar
• Lu, C. W. and Reynolds, M. R. (1999). EWMA control charts for monitoring the mean of autocorrelated processes. Journal of Quality Technology, 31, 166–188.
   Google Scholar
• Lu, C. W. and Reynolds, M. R. (2001). CUSUM charts for monitoring an autocorrelated process. Journal of Quality Technology, 33, 316–334.
   Google Scholar
• Lowry, C. A. and Montgomery, D. C. (1995). A review of multivariate control charts. IIE Transactions, 27, 800–810.
   Google Scholar
• Lowry, C. A., Woodall, W. H., Champ, C. W. and Rigdon, S. E. (1992). A multivariate exponentially weighted moving average control chart. Technometrics, 34, 12–21.
   Google Scholar
• Mason, R. L., Tracy, N. and Young, J. C. (1997). A practical approach for interpreting multivariate T squared control chart signals. Journal of Quality Technology, 29, 396–406.
   Google Scholar
• Mason, R. L. and Young, J. C. (1999). Improving the sensitivity of the T2 statistic in multivariate process control. Journal of Quality Technology, 31, 155–188.
   Google Scholar
• Montgomery, D. C. (2005). Introduction to Statistical Quality Control, 5th edition. Wiley, NY.
   Google Scholar
• Montgomery, D. C. and Friedman, D. J. (1989). Statistical process control in a computer-integrated manufacturing environment. Statistical Process Control in Automated Manufacturing, Marcel Dekker, New York, NY, 67–87.
   Google Scholar
• Montgomery, D. and Mastrangelo, C. (1991). Some staistical process control methods for autocorrelated Data. Journal of Quality Technology, 23, 179–193.
   Google Scholar
• Montgomery, D. C. and Wadsworth, Jr., H. M. (1972). Some techniques for multivariate quality control applications. ASQC Technical Conference Transactions, Washington, DC, May, 427–435.
   Google Scholar
• Pignatiello, J. J. and Runger, G. C. (1990). Comparison of multivariate CUSUM charts. Journal of Quality Technology, 22, 173–186.
   Google Scholar
• Pole, A., West, M and Harrison, J. (1994). Applied Bayesian Forecasting and Time Series Analysis, Chapman & Hall, NY.
   Google Scholar
• Prybutok, V. R., Clayton, H. and Harvey, M. M., (1997). Comparison of fixed versus variable sampling interval Shewhart X over-bar control charts in the presence of positively autocorrelated Data. Communications in Statistics, 26, 83–106.
   Google Scholar
• Prabhu, S. S. and Runger, G. C. (1997). Designing a multivariate EWMA control chart. Journal of Quality Technology, 29, 8–15.
   Google Scholar
• Quintana, J. M. and West, M. (1988). Time series analysis of compositional data. Bayesian Statistics, 3, 747–756.
   Google Scholar
• Radson, D. (1997). Moving-range chart and autocorrelated processes. Communications in Statistics, 26, 2113–2127.
   Google Scholar
• Runger, G. (1996). Projections and the U squared multivariate control chart. Journal of Quality Technology, 28, 313–319.
   Google Scholar
• Runger, G., Alt, F. B. and Montgomery, D. C. (1996). Contributors to a multivariate statistical process control chart signal. Communications in Statistics, 25.
   Google Scholar
• Runger, G. and Willemain, T. R. (1995). Model-based and model-free control of autocorrelated processes, Journal of Quality Technology, 27, 283–292.
   Google Scholar
• Sullivan, J. H. and Woodall, W. (1996). A comparison of multivariate control charts for individual observations. Journal of Quality Technology, 28, 398–408.
   Google Scholar
• Tracy, N., Young, J. C. and Mason, R. L. (1992). “Multivariate control charts for individual observations. Journal of Quality Technology, 24, 88–95.
   Google Scholar
• VanBrackle, L. N. and Reynolds, M. (1997). EWMA and CUSUM control charts in the presence of correlation. Communications in Statistics, 26, 979–1008.
   Google Scholar
• West, M. and Harrison, J. (1997). Bayesian Forecasting and Dynamic Models, Springer, NY.
   Google Scholar
• Wright, C. M., Booth, D. E. and Hu, M. Y. (2001). Joint estimation: SPC method for short-run autocorrelated data. Journal of Quality Technology, 33, 365–378.
   Google Scholar
• Yeh, A. B., Huwang, L. and Wu, Y.-F. (2004). A likelihood-ratio-based EWMA control chart for monitoring variability of multivariate normal processes. IIE Transactions, 9, 865–880.
   Google Scholar
• Yourstone, S. R. and Montgomery, D. C. (1989). Development of a real-time statistical process control algorithm. Quality and Reliability Engineering International, 5, 309–317.
   Google Scholar
• Zhang, N. F. (1998). A statistical control chart for stationary process data. Technometrics, 40, 24–40.
   Google Scholar