Abstract :

A control chart used with MA control chart to track the number of faulty products or faults suggested. When the characteristics of quality of interest obey a Poisson distribution. A specified number of objects are observed at various time intervals in order to observe the number of non-conformities. By measuring ARLs under different sample sizes and parameters by considering ARLs in power, the output of the proposed chart is evaluated. It should be noted The proposed control chart seems to be more reliable than the traditional current control charts in detecting small adjustments in the manufacture process.

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Keywords :

Average run length, Control chart, Moving Average, Poisson distribution

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References :

1. D. C. Montgomery, “Introduction to statistical quality control,” Wiley, 2009.
2. M. Martin, The map and the rope: Finnish nominal inflection as a learning target, University of Jyv, 1995.
3. M. R. R. J. T. P. R. a. W. Z. G. Stoumbos, “The state of statistical process control as we proceed into 21st century,” Journal of American statistical Association, pp. 992 – 998, 2000.
4. H. B. Wong, F. F. Gan, and T.C Chang, “Design of moving average control chart,” jounal of statistical computer simulation, pp. 47 – 62, 2004.
5. S.Knoth, “Accurate ARL calculation for EWMA control charts monitoring normal mean and variance simultaneously.,” Sequential Anal. Vol. 26, pp. 251-263, 2007.
6. R.S.Sparks, “A group of moving averages control plan for signaling varying shifts,” Quality Eng., pp. 519-532, 2003.
7. M. a. P.Yap, “Joint monitoring of process mean and variability with a single moving average control chart,” Quality Eng., pp. 51-65, 2007.
8. M. B. K. a. P. Yap, “Joint monitoring of process mean and vaariability with a single moving average control chart,” Quality Engeenring, vol, 17, pp. 51 – 65, 2007.
9. L. C. J. a. A. M.Aslam, “A control chart for COMU Poisson distribution using multiple dependent tate sampling,” Quality Rel. Eng. Int, pp. 2803-2812, 2016.
10. M. A. a. C. H. J. L. Ahmad, “Designing of X-bar control charts based on process capability index using repetitive sampling,” Trans. Ins. Meas. Control, vol 36, pp. 367 – 374, 2013.
11. R. S. Sparks, “A group of moving averages control plan for signaling varying location shifts,” Quality Engneering, pp. 519 – 532, 2003.
12. M. B. K. a. V. Wong, “A double moving average control chart,” Communication statistical simulation computer, vol 37, pp. 1696 – 1708, 2008.
13. G. R. D. M. a. K. S. W. Molanau, “A program for ARL calculation for multivariate EWMA charts,” Journal of quality technology, vol 33, p. 515, 2001.
14. S. S. a. Y. A. C. Chananet, “The ARL of EWMA chart for monitoring ZINB model using Markov chain approach,” Jounal of applied physics mathematics, p. 236, 2014.
15. M. Aslam, “A mixed EWMAUCUSUM control chart for weibull disttributed quality characteristics,” Quality Rel. Eng. Int., vol, 32, pp. 2987 – 2994, 2016.
16. F. A. S. a. H. X. J. C. Fu, “On the average run lenght of quality control schemes using a Markov chain approach,” Jounal of statistical probability, pp. 369 – 380, 2002.
17. W. A. Shewhart, “Economic control of quality of manufactured product,” American society for quality control, 1931.
18. F. T. C. H.B Wong, “Design of Moving Average Control Chart,” J.Statist. Comput. Simul, pp. 47-62, 2004.
19. G. a. G.Masarotto, “An adoptive exponentially weighted moving average control chart,” Technometrics, pp. 199-207, 2003.
20. J. C. Benneyan, “Use and interpretation of statistical quality control charts,” International Journal for Quality in Health Care, vol. 10, pp. 69-73, 1998.