In some statistical process monitoring applications, the quality of a product or process can be determined by a linear or nonlinear regression relationship called "profile". Basically, standard monitoring methods involve two phases: Phase I and Phase II. Usually, it is assumed that the process parameters are known, however this condition in many applications is not met and parameters are estimated using the in-control data set collected in Phase I. The present study evaluates and compares some Phase II control chart approaches for monitoring the second order polynomial profiles when the process parameters are estimated. These methods includes Orthogonal, MEWMA and dEWMA-OR control charts. The performance of each control chart is measured in terms of ARL, SDRL, AARL and SDARL metrics using Monte Carlo simulation approach. The results showed that the in-control and out-of-control performance of control charts is strongly affected by parameter estimation, especially when only a few Phase I samples are used to estimate the parameters. Moreover, the superior overall performance of the Orthogonal method rather than the other competing methods is shown. Furthermore, we concluded that F estimation method leads to better performance of control charts in Phase II.
[1] Montogomery, D., Introduction to statistical quality control 6th edition. Arizona state University, 2009.
[2] Kang, L. and S.L. Albin, On-line monitoring when the process yields a linear profile. Journal of quality Technology, 2000. 32(4): p. 418.
[3] Kim, , M.A. Mahmoud, and W.H. Woodall, On the monitoring of linear profiles. Journal of Quality Technology, 2003. 35(3): p. 317.
[4] Stover, F.S. and R.V. Brill, Statistical quality control applied to ion chromatography calibrations. Journal of Chromatography A, 1998. 804(1-2): p. 37-43.
[5] Mahmoud, M.A. and W.H. Woodall, Phase I analysis of linear profiles with calibration applications. Technometrics, 2004. 46(4): p. 380-391.
[6] Mahmoud, M.A., Parker, P.A., Woodall, W.H., Hawkins, D.M., A change point method for linear profile data. Quality and Reliability Engineering International, 2007. 23(2): p. 247-268.
[7] Mahmoud, M.A., J. Morgan, and W.H. Woodall, The monitoring of simple linear regression profiles with two observations per sample. Journal of Applied Statistics, 2010. 37(8): p. 1249-1263.
[8] Noorossana, R., A. Amiri, and P. Soleimani, On the monitoring of autocorrelated linear profiles. Communications in Statistics—Theory and Methods, 2008. 37(3): p. 425-442.
[9] Mahmoud, M.A., Phase I analysis of multiple linear regression profiles. Communications in Statistics—Simulation and Computation®, 2008. 37(10): p. 2106-2130.
[10] Jensen, W.A., J.B. Birch, and W.H. Woodall, Monitoring correlation within linear profiles using mixed models. Journal of Quality Technology, 2008. 40(2): p. 167-183.
[11] Amiri, A., Eyvazian, M., Zou, C., Noorossana, R., A parameters reduction method for monitoring multiple linear regression profiles. The International Journal of Advanced Manufacturing Technology, 2012. 58(5-8): p. 621-629.
[12] Parker, P.A. and T.D. Finley, Advancements in aircraft model force and attitude instrumentation by integrating statistical methods. Journal of aircraft, 2007. 44(2): p. 436-443.
[13] Noorossana, R., M. Eyvazian, and A. Vaghefi, Phase II monitoring of multivariate simple linear profiles. Computers & Industrial Engineering, 2010. 58(4): p. 563-570.
[14] Eyvazian, M., Noorossana, R., Saghaei, A., Amiri, A., Phase II monitoring of multivariate multiple linear regression profiles. Quality and Reliability Engineering International, 2011. 27(3): p. 281-296.
[15] Ayoubi, M., R. Kazemzadeh, and R. Noorossana, Estimating multivariate linear profiles change point with a monotonic change in the mean of response variables. The International Journal of Advanced Manufacturing Technology, 2014. 75(9-12): p. 1537-1556.
[16] Zou, C., X. Ning, and F. Tsung, LASSO-based multivariate linear profile monitoring. Annals of Operations Research, 2012. 192(1): p. 3-19.
[17] Ding, Y., L. Zeng, and S. Zhou, Phase I analysis for monitoring nonlinear profiles in manufacturing processes. Journal of Quality Technology, 2006. 38(3): p. 199-216.
[18] Williams, J.D., W.H. Woodall, and J.B. Birch, Statistical monitoring of nonlinear product and process quality profiles. Quality and Reliability Engineering International, 2007. 23(8): p. 925-941.
[19] Vaghefi, A., S. Tajbakhsh, and R. Noorossana, Phase II monitoring of nonlinear profiles. Communications in Statistics—Theory and Methods, 2009. 38(11): p. 1834-1851.
[20] Williams, J.D., W.H. Woodall, and J.B. Birch. Phase I monitoring of nonlinear profiles. in quality and productivity research conference, Yorktown Heights, New York. 2003.
[21] Woodall, W.H. and D.C. Montgomery, Research issues and ideas in statistical process control. Journal of Quality Technology, 1999. 31(4): p. 376.
[22] Chen, G., The mean and standard deviation of the run length distribution of X charts when control limits are estimated. Statistica Sinica, 1997: p. 789-798.
[23] Chakraborti, S., Run length distribution and percentiles: the Shewhart chart with unknown parameters. Quality Engineering, 2007. 19(2): p. 119-127.
[24] Albers, W. and W.C. Kallenberg, Estimation in Shewhart control charts: effects and corrections. Metrika, 2004. 59(3): p. 207-234.
[25] Jones,A., C.W. Champ, and S.E. Rigdon, The performance of exponentially weighted moving average charts with estimated parameters. Technometrics, 2001. 43(2): p. 156-167.
[26] Jones, L.A., C.W. Champ, and S.E. Rigdon, The run length distribution of the CUSUM with estimated parameters. Journal of Quality Technology, 2004. 36(1): p. 95-108.
[27] Maravelakis, P.E. and P. Castagliola, An EWMA chart for monitoring the process standard deviation when parameters are estimated. Computational statistics & data analysis, 2009. 53(7): p. 2653-2664.
[28] Mahmoud, M.A. and P.E. Maravelakis, The performance of the MEWMA control chart when parameters are estimated. Communications in Statistics—Simulation and Computation®, 2010. 39(9): p. 1803-1817.
[29] Shu, L., F. Tsung, and K.-L. Tsui, Run-length performance of regression control charts with estimated pa Journal of Quality Technology, 2004. 36(3): p. 280-292.
[30] Castagliola, P. and P.E. Maravelakis, A CUSUM control chart for monitoring the variance when parameters are estimated. Journal of Statistical Planning and Inference, 2011. 141(4): p. 1463-1478.
[31] Champ, C.W., L.A. Jones-Farmer, and S.E. Rigdon, Properties of the T 2 control chart when parameters are estimated. Technometrics, 2005. 47(4): p. 437-445.
[32] Castagliola, P., P.E. Maravelakis, and F.O. Figueiredo, The EWMA median chart with estimated parameters. IIE Transactions, 2016. 48(1): p. 66-74.
[33] Jones, M.A. andH. Steiner, Assessing the effect of estimation error on risk-adjusted CUSUM chart performance. International Journal for Quality in Health Care, 2012. 24(2): p. 176-181.
[34] Zhang, M., F.M. Megahed, and W.H. Woodall, Exponential CUSUM charts with estimated control limits. Quality and Reliability Engineering International, 2014. 30(2): p. 275-286.
[35] Zhang, M., Peng, Y., Schuh, A., Megahed, F.M., Woodall, W.H., Geometric charts with estimated control limits. Quality and Reliability Engineering International, 2013. 29(2): p. 209-223.
[36] Aly, A.A., M.A. Mahmoud, and W.H. Woodall, A comparison of the performance of phase II simple linear profile control charts when parameters are estimated. Communications in Statistics-Simulation and Computation, 2015. 44(6): p. 1432-1440.
[37] Woodall, W.H. and D.C. Montgomery, Some current directions in the theory and application of statistical process monitoring. Journal of Quality Technology, 2014. 46(1): p. 78-94.
[38] Mahmoud, M.A., The performance of phase II simple linear profile approaches when parameters are estimated. Communications in Statistics-Simulation and Computation, 2012. 41(10): p. 1816-1833.
[39] Yazdi, A.A., Z. Hamadani, and A. Amiri, Phase II monitoring of multivariate simple linear profiles with estimated parameters. Journal of Industrial Engineering International, 2019. 15(4): p. 557-570.
[40] Aly, A.A., M.A. Mahmoud, and R. Hamed, The performance of the multivariate adaptive exponentially weighted moving average control chart with estimated parameters. Quality and Reliability Engineering International, 2016. 32(3): p. 957-967.
[41] Kazemzadeh, R.B., R. Noorossana, and A. Amiri, Phase I monitoring of polynomial profiles. Communications in Statistics—Theory and Methods, 2008. 37(10): p. 1671-1686.
[42] Zou, C., F. Tsung, and Z. Wang, Monitoring general linear profiles using multivariate exponentially weighted moving average schemes. Technometrics, 2007. 49(4): p. 395-408.
[43] Kazemzadeh, R., R. Noorossana, and A. Amiri, Monitoring polynomial profiles in quality control applications. The International Journal of Advanced Manufacturing Technology, 2009. 42(7-8): p. 703-712.
[44] Amiri, A., W.A. Jensen, and R.B. Kazemzadeh, A case study on monitoring polynomial profiles in the automotive industry. Quality and Reliability Engineering International, 2010. 26(5): p. 509-520.
[45] Kazemzadeh, R., R. Noorossana, and A. Amiri, Phase II monitoring of autocorrelated polynomial profiles in AR (1) processes. Scientia Iranica. Transaction E, Industrial Engineering, 2010. 17(1): p. 12.
[46] Abdella, G.M., Kim, J., Al-Khalifa, K.N., Hamouda, A.M.., Double EWMA‐Based Polynomial Quality Profiles Monitoring. Quality and Reliability Engineering International, 2016. 32(8): p. 2639-2652.
[47] Lowry, C.A., Woodall, W.H., Champ, C.W., Rigdon, S.E., A multivariate exponentially weighted moving average control chart. Technometrics, 1992. 34(1): p. 46-53.
[48] Crowder, S.V. and M.D. Hamilton, An EWMA for monitoring a process standard deviation. Journal of Quality Technology, 1992. 24(1): p. 12-21.
[49] Shamma, S.E., R.W. Amin, and A.K. Shamma, A double exponentially weigiited moving average control procedure with variable sampling intervals. Communications in Statistics-Simulation and Computation, 1991. 20(2-3): p. 511-528.
[50] Montgomery, D.C., E.A. Peck, and G.G. Vining, Introduction to linear regression analysis. Vol. 821. 2012: John Wiley & Sons.
[51] Quesenberry, C., The effect of sample size on estimated limits for X ̅ and X control charts. Journal of Quality Technology, 1993. 25: p. 237–247.
[52] Jones, L.A., The statistical design of EWMA control charts with estimated parameters. Journal of Quality Technology, 2002. 34: p. 277–288.
[53] Mahmoud, M.A., Maravelakis, P.E., The performance of the MEWMA control chart when parameters are estimated. Communications in Statistics-Simulation and, 2010. 39: p. 1803–1817.
Ghasemi Eshkaftaki, Z., Zeinal Hamadani, A., & Ahmadi Yazdi, A. (2021). Evaluating Parameter Estimation Effect on the Polynomial Profile Monitoring Methods’ Phase II Performance. Advances in Industrial Engineering, 55(2), 133-150. doi: 10.22059/jieng.2021.326559.1785
MLA
Zohre Ghasemi Eshkaftaki; Ali Zeinal Hamadani; Ahmad Ahmadi Yazdi. "Evaluating Parameter Estimation Effect on the Polynomial Profile Monitoring Methods’ Phase II Performance", Advances in Industrial Engineering, 55, 2, 2021, 133-150. doi: 10.22059/jieng.2021.326559.1785
HARVARD
Ghasemi Eshkaftaki, Z., Zeinal Hamadani, A., Ahmadi Yazdi, A. (2021). 'Evaluating Parameter Estimation Effect on the Polynomial Profile Monitoring Methods’ Phase II Performance', Advances in Industrial Engineering, 55(2), pp. 133-150. doi: 10.22059/jieng.2021.326559.1785
VANCOUVER
Ghasemi Eshkaftaki, Z., Zeinal Hamadani, A., Ahmadi Yazdi, A. Evaluating Parameter Estimation Effect on the Polynomial Profile Monitoring Methods’ Phase II Performance. Advances in Industrial Engineering, 2021; 55(2): 133-150. doi: 10.22059/jieng.2021.326559.1785