An Optimal Filter Design Approach to Statistical Process Control

References

• Apley, D. W. and Shi, J. (1999). “The GLRT for Statistical Process Control of Autocorrelated Processes”. IIE Transactions 31, pp. 1123–1134.
   Web of Science ®Google Scholar
• Box, G. E. P.; Jenkins, G.; and Reinsel, G. (1994). Time Series Analysis, Forecasting, and Control, 3rd ed., Prentice-Hall, Englewood Cliffs, NJ.
   Google Scholar
• Box, G. E. P. and Luceño, A. (1997). Statistical Control by Monitoring and Feedback Adjustment. Wiley, New York, NY.
   Google Scholar
• Box, G. E. P. and Ramírez, J. (1992). “Cumulative Score Charts”. Quality and Reliability Engineering International 8, pp. 17–27.
   Google Scholar
• Brook, D. and Evans, D. A. (1972). “An Approach to the Probability Distribution of CUSUM Run Lengths”. Biometrika 59, pp. 539–549.
   Web of Science ®Google Scholar
• Chin, C. (2004). “Optimal Filter Design Approaches to Statistical Process Control for Autocorrelated Processes”. Unpublished Ph.D. Dissertation, Texas A&M University.
   Google Scholar
• Chin, C. and Apley, D. W. (2006). “Optimal Design of Second-Order Linear Filters for Control Charting”. Technometrics 48 (3), pp. 337–348.
   Web of Science ®Google Scholar
• Jiang, W.; Tsui, K. and Woodall, W. H. (2000). “A New SPC Monitoring Method: The ARMA Chart”. Technometrics 42, pp. 399–410.
   Web of Science ®Google Scholar
• Jiang, W.; Wu, H.; Tsung, F.; Nair, V. N.; and Tsui, K. (2002). “Proportional Integral Derivative Charts for Process Monitoring”. Technometrics 44, pp. 205–214.
   Web of Science ®Google Scholar
• Johnson, R. A. and Wichern, D. W. (2002). Applied Multivariate Statistical Analysis, 5th ed. Prentice Hall, Upper Saddle River, NJ.
   Google Scholar
• Lu, C. and Reynolds, M. R., Jr. (1999a). “EWMA Control Charts for Monitoring the Mean of Autocorrelated Processes”. Journal of Quality Technology 31, pp. 166–188.
   Web of Science ®Google Scholar
• Lu, C. and Reynolds, M. R., Jr. (1999b). “Control Charts for Monitoring the Mean and Variance of Autocorrelated Processes”. Journal of Quality Technology 31, pp. 259–274.
   Web of Science ®Google Scholar
• Lucas, J. M. and Saccucci, M. S. (1990). “Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements”. Technometrics 32, pp. 1–12.
   Web of Science ®Google Scholar
• Luceño, A. (1999). “Average Run Lengths and Run Length Probability Distributions for CUSCORE Charts to Control Normal Mean”. Computational Statistics and Data Analysis 32, pp. 177–195.
   Web of Science ®Google Scholar
• Luceño, A. (2004). “CUSCORE Charts to Detect Level Shifts in Autocorrelated Noise”. Quality Technology and Quantitative Management 1, pp. 27–45.
   Google Scholar
• Luenberger, D. G. (1984). Linear and Nonlinear Programming, 2nd ed. Addison-Wesley, Reading, MA.
   Google Scholar
• Moustakides, G. (1986). “Optimal Stopping Times for Detecting Changes in Distributions”. Annals of Statistics 14, pp. 1379–1387.
   Web of Science ®Google Scholar
• Pandit, S. M. and Wu, S. M. (1983). Time Series and System Analysis with Applications. John Wiley & Sons, New York, NY.
   Google Scholar
• Reynolds, M. R., Jr. and Stoumbos, Z. (2001). “Monitoring the Process Mean and Variance Using Individual Observations and Variable Sampling Intervals”. Journal of Quality Technology 33, pp. 181–205.
   Web of Science ®Google Scholar
• Roberts, S. W. (1959). “Control Chart Tests Based on Geometric Moving Averages”. Technometrics 1, pp. 239–250.
   Google Scholar
• Runger, G. C. and Testik, M. C. (2003). “Control Charts for Monitoring Fault Signatures: CUSCORE versus GLR”. Quality and Reliability Engineering International 19, pp. 387–396.
   Web of Science ®Google Scholar
• Shu, L.; Apley, D. W.; and Tsung, F. (2002). “Autocorrelated Process Monitoring Using Triggered CUSCORE Charts”. Quality and Reliability Engineering International 18, pp. 411–421.
   Web of Science ®Google Scholar
• Sparks, R. S. (2000), “CUSUM Charts for Signalling Varying Location Shifts”. Journal of Quality Technology 32, pp. 157–171.
   Web of Science ®Google Scholar
• Superville, C. R. and Adams, B. M. (1994). “An Evaluation of Forecast-Based Quality Control Schemes”. Communications in Statistics: Simulation and Computation 23, pp. 645–661.
   Web of Science ®Google Scholar
• Vander Wiel, S. A. (1996), “Monitoring Processes That Wander Using Integrated Moving Average Models”. Technometrics 38, pp. 139–151.
   Web of Science ®Google Scholar
• Wardell, D. G.; Moskowitz, H.; and Plante, R. D. (1994). “Run-Length Distributions of Special-Cause Control Charts for Correlated Processes”. Technometrics 36, pp. 3–17.
   Web of Science ®Google Scholar
• Zhang, N. F. (1998). “A Statistical Control Chart for Stationary Process Data”. Technometrics 40, pp. 24–38.
   Web of Science ®Google Scholar