A maximum adaptive exponentially weighted moving average control chart for monitoring process mean and variability

References

• Abbas, N., Riaz, M., & Does, R. J. M. M. (2014). Memory-type control charts for monitoring the process dispersion. Quality and Reliability Engineering International, 30(5), 623–632.
   Web of Science ®Google Scholar
• Abbasi, S. A., & Miller, A. (2013). MDEWMA chart: An efficient and robust alternative to monitor process dispersion. Journal of Statistical Computation and Simulation, 83(2), 247–268.
   Web of Science ®Google Scholar
• Acosta-Mejia, C. A., Pignatiello, J. J., Jr, & Rao, B. V. (1999). A comparison of control charting procedures for monitoring process dispersion. IIE Transactions, 31(6), 569–579.
   Web of Science ®Google Scholar
• Calzada, M. E., & Scariano, S. M. (2013). The synthetic t and synthetic EWMA t charts. Quality Technology & Quantitative Management, 10(1), 37–56.
   Web of Science ®Google Scholar
• Capizzi, G., & Masarotto, G. (2003). An adaptive exponentially weighted moving average control chart. Technometrics, 45(3), 199–207.
   Web of Science ®Google Scholar
• Castagliola, P. (2005). A new S2-EWMA control chart for monitoring the process variance. Quality and Reliability Engineering International, 21(8), 781–794.
   Web of Science ®Google Scholar
• Chen, G., Cheng, S. W., & Xie, H. (2001). Monitoring process mean and variability with one EWMA chart. Journal of Quality Technology, 33(2), 223–233.
   Web of Science ®Google Scholar
• Chen, G., Cheng, S. W., & Xie, H. (2004). A new EWMA control chart for monitoring both location and dispersion. Quality Technology & Quantitative Management, 1(2), 217–231.
   Google Scholar
• Crowder, S. V., & Hamilton, M. D. (1992). An EWMA for monitoring a process standard deviation. Journal of Quality Technology, 24(1), 12–21.
   Web of Science ®Google Scholar
• Haq, A. (2017). New synthetic CUSUM and synthetic EWMA control charts for monitoring the process mean using auxiliary information. Quality and Reliability Engineering International, 33(7), 1549–1565.
   Web of Science ®Google Scholar
• Haq, A. (2018). A new adaptive EWMA control chart for monitoring the process dispersion. Quality and Reliability Engineering International, earlyaccess. 34(5), 846-857.
   Web of Science ®Google Scholar
• Haq, A., Brown, J., & Moltchanova, E. (2016). New synthetic EWMA and synthetic CUSUM control charts for monitoring the process mean. Quality and Reliability Engineering International, 32(1), 269–290.
   Web of Science ®Google Scholar
• Haq, A., Gulzar, R., & Khoo, M. B. C. (2018). An efficient adaptive EWMA control chart for monitoring the process mean. Quality and Reliability Engineering International, 34(4), 563–571.
   Web of Science ®Google Scholar
• Huwang, L., Huang, C.-J., & Wang, Y.-H. T. (2010). New EWMA control charts for monitoring process dispersion. Computational Statistics & Data Analysis, 54(10), 2328–2342.
   Web of Science ®Google Scholar
• Khoo, M. B. C., Teh, S. Y., & Wu, Z. (2010). Monitoring process mean and variability with one double EWMA chart. Communications in Statistics - Theory and Methods, 39(20), 3678–3694.
   Web of Science ®Google Scholar
• Maravelakis, P. E., Castagliola, P., & Khoo, M. B. C. (2017). Run length properties of run rules EWMA chart using integral equations. Quality Technology & Quantitative Management, 0(0), 1–11.
   Google Scholar
• Montgomery, D. C. (2009). Introduction to statistical quality control (6th ed.). New York: Wiley.
   Google Scholar
• Page, E. S. (1954). Continuous inspection schemes. Biometrika, 41(1/2), 100–115.
   Web of Science ®Google Scholar
• Quesenberry, C. P. (1995). On properties of Q charts for variables. Journal of Quality Technology, 27(3), 184–203.
   Web of Science ®Google Scholar
• Roberts, S. W. (1959). Control chart tests based on geometric moving averages. Technometrics, 1(3), 239–250.
   Google Scholar
• Sheu, S.-H., Huang, C.-J., & Hsu, T.-S. (2012). Extended maximum generally weighted moving average control chart for monitoring process mean and variability. Computers & Industrial Engineering, 62(1), 216–225.
   Web of Science ®Google Scholar
• Sheu, S.-H., & Lin, T.-C. (2003). The generally weighted moving average control chart for detecting small shifts in the process mean. Quality Engineering, 16(2), 209–231.
   Google Scholar
• Shu, L. (2008). An adaptive exponentially weighted moving average control chart for monitoring process variance. Journal of Statistical Computation and Simulation, 78(4), 367–384.
   Web of Science ®Google Scholar
• Sparks, R. S. (2000). CUSUM charts for signalling varying location shifts. Journal of Quality Technology, 32(2), 157–171.
   Web of Science ®Google Scholar
• Takemoto, Y., & Arizono, I. (2018). Information visualization about changes of process mean and variance on (x̅, s) control chart. Quality Technology & Quantitative Management, 0(0), 1–15.
   Google Scholar
• Tang, A., Castagliola, P., Sun, J., & Hu, X. (2018). Optimal design of the adaptive EWMA chart for the mean based on median run length and expected median run length. Quality Technology & Quantitative Management, 0(0), 1–20.
   Google Scholar
• Wu, Z., & Tian, Y. (2005). Weighted-loss-function CUSUM chart for monitoring mean and variance of a production process. International Journal of Production Research, 43(14), 3027–3044.
   Web of Science ®Google Scholar
• Xie, H. (1999). Contributions to qualimentry (PhD thesis). Department of Statistics, The University of Manitoba, Winnipeg, Manitoba, Canada.
   Google Scholar