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AEWMA t Control Chart for Short Production Runs
Zhiyuan CHANG, Jinsheng SUN
Journal of Systems Science and Information ›› 2016, Vol. 4 ›› Issue (5) : 444-459.
PDF(165 KB)
PDF(165 KB)
AEWMA t Control Chart for Short Production Runs
Owing to the limited number of inspections during a short run process, it is impossible to get the correct estimate of the population mean and standard deviation during Phase I implementation of control chart. The t control chart proposed recently can overcome this problem. The EWMA t control chart has been proposed to monitor the process mean, but a single EWMA t control chart cannot perform well for small and large shifts simultaneously, which is known as the "inertia problem". The adaptive varying smoothing parameter EWMA (AEWMA) control chart can overcome the inertia problem. In this paper, the AEWMA t control chart for short run process is proposed. The truncated average run length and the probability of trigger a signal are adopted to test the performance of short run AEWMA t chart. Based on the investigation of the joint effect of control chart parameters on the performance of AEWMA t chart, a new optimization algorithm is proposed for statistical design of the AEWMA control chart. Simulations are performed for perfect and imperfect setup conditions, the results show that the AEWMA t control chart performs better than the EWMA t control chart.
AEWMA control chart / t chart / short run / TARL / Markov chain {{custom_keyword}} /
[1] Quesenberry C P. The effect of sample size on estimated limits for X and X control charts. Journal of Quality Technology, 1993, 25(4):237-247.
[2] Neduraman G, Pigniatiello J J. On estimating X control chart limits. Journal of Quality Technology, 2001, 33(3):206-212.
[3] Tsai T R, Lin H C, Wu S J. An alternative control chart approach based on small numbers of subgroups. International Journal of Information and Manufacturing Sciences, 2004, 15(4):61-73.
[4] Tsai T R, Lin J J, Wu S J, et al. On estimating control limits of X chart when the number of subgroups is small. International Journal of Advanced Manufacturing Technology, 2005, 26(11):1312-1316.
[5] Quesenberry C P. SPC Q charts for star-up processes and short or long runs. Journal of Quality Technology, 1991, 23(3):213-224.
[6] Quesenberry C P. On properties for Q charts for variables. Journal of Quality Technology, 1995, 27(3):184-203.
[7] Castillo E D, Montgomery D C. Short-run statistical process control:Q-chart enhancements and alternative methods. Quality and Reliability Engineering International, 1994, 10(2):87-97.
[8] He F, Jiang W, Shu L. Improved self-starting control charts for short runs. Quality Technology and Quantitative Management, 2008, 5(3):289-308.
[9] Celano G, Castagliola P, Trovato E, et al. Shewhart and EWMA t control charts for short production runs. Quality and Reliability Engineering International, 2011, 27(3):313-326.
[10] Zhang L, Chen G, Castagliola P. On t and EWMA t charts for monitoring changes in the process mean. Quality and Reliability Engineering International, 2009, 25(8):933-945.
[11] Kazemzadeh R B, Karbasian M, Babakhani M A. An EWMA t chart with variable sampling intervals for monitoring the process mean. The International Journal of Advanced Manufacturing Technology, 2013, 66(1-4):125-139.
[12] Nenes G, Tagaras G. The CUSUM chart for monitoring short production runs. International Journal of Production Research, 2005, 44(8):1569-1587.
[13] Celano G, Castagliola P, Fichera S, et al. Performance of t control charts in short runs with unknown shift sizes. Computers and industrial Engineering, 2013, 64(1):56-68.
[14] Celano G, Castagliola P, Trovato E, et al. The economic performance of the shewhart t chart. Quality and Reliability Engineering International, 2012, 28(2):159-180.
[15] Celano G, Castagliola P, Trovato E. The economic performance of a CUSUM t Control Chart for Monitoring Short Production Runs. Quality Technology and Quantitative Management, 2012, 9(4):329-354.
[16] Capizzi G, Masarotto G. An adaptive exponentially weighted moving average control chart. Technometrics, 2003, 45(3):199-207.
[17] Woodall W H, Mahmoud M A. The inertial properties of quality control charts. Technometrics, 2005, 47(4):425-436.
[18] Shu L J. An adaptive exponentially weighted moving average control chart for monitoring process variances. Journal of Statistical Computation and Simulation, 2008, 78(4):367-384.
[19] Saleh N A, Mahmoud M A, Abdel-Salam G A-S. The performance of the adaptive exponentially weighted moving average control chart with estimated parameters. Quality and Reliability Engineering International, 2013, 29(4):595-606.
[20] Yashchin E. Estimating the current mean of a process subject to abrupt changes. Technometrics, 1995, 37(3):311-323.
[21] Su Y, Shu L, Tsui K L. Adaptive EWMA procedures for monitoring processes subject to linear drifts. Computational Statistics and Data Analysis, 2011, 55(10):2819-2829.
Supported by the National Natural Science Foundation of China (70931002)
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