Monitoring of Mean Processes with Mixed Moving Average – Modified Exponentially Weighted Moving Average Control Charts

Khanittha Talordphop, Saowanit Sukparungsee, Yupaporn Areepong

Abstract

In Statistical Process Control, a control chart is the most effective equipment for monitoring and improving processes. Classic control charts were created in the past and were effective at detecting both small and large changes. However, the mixed control chart has been presented to improve the performance of the traditional control chart. This research introduces a new mixed control chart, MA-MEWMA, which combines the moving average (MA) and the modified exponentially weighted moving average (MEWMA) charts to detect the tiny changes in the procedures both of symmetric and asymmetric distributions. The average run length (ARL) can also be used to measure progress in the MA-MEWMA chart with Shewhart, MA, and MEWMA charts that employ Monte Carlo simulation. The experiments demonstrated that the proposed chart had a greater impact compared to all other control charts with the parameter level ±0.05, ±0.10, ±0.25, ±0.50, ±0.75, ±1.00, ±1.50 through discovering a change in the average of the method in the control where ARL₀ = 370. On the other hand, when the parameter level was set to 2.00, ±3.00, ±4.00, the MA control chart performed admirably. An excellent example is data set on viscosity from a batch chemical process. Environmental information data were provided to explain how the suggested chart and MA-MEWMA charts are implemented, demonstrating that the MA-MEWMA chart was more successful than other charts in detecting changes.

Keywords

References

[1] W. A. Shewhart, Economic Control of Quality Manufactured Product. New York: D. Van Nostrand Company, 1930.

[2] S. W. Roberts, “Control chart tests based on geometric moving average,” Techmometrics, vol. 1, no. 3, pp. 239–250, 1959.

[3] E. S. Page, “Continuous inspection schemes,” Biometrika, vol. 41, no. 1–2, pp. 100–115, 1954.

[4] A. K. Patel and J. Divecha, “Modified exponentially weighted moving average (EWMA) control chart for an analytical process data,” Journal of Chemical Engineering and Materials Science, vol. 2, no. 1, pp. 12–20, 2011.

[5] N. Khan, M. Aslam, and C.-H. Jun, “Design of a control chart using a modified EWMA statistic,” Quality and Reliability Engineering International, vol. 33, no. 5, pp. 1095–1104, 2017.

[6] M. B. C. Khoo, “Moving average control chart for monitoring the fraction non-conforming,” Quality and Reliability Engineering International, vol. 20, no. 6, pp. 617–635, 2004.

[7] H. B. Wong, F. F. Gan, and T. C. Chang, “Designs of moving average control chart,” Journal of Statistical Computational and Simulation, vol. 74, no. 1, pp. 47–62, 2004.
[8] R. Taboran, S. Sukparungsee, and Y. Areepong, “Mixed moving average – Exponentially weighted moving average control charts for monitoring of parameter change,” in Proceedings of the International MultiConference of Engineers and Computer Scientists 2019, 2019, pp.1–5.

[9] S. Sukparungsee, Y. Areepong, and R. Taboran, “Exponentially weighted moving average – moving average charts for monitoring the process mean,” PLOS ONE, vol. 15, no. 2, 2020. doi: 10.1371/ journal.pone.0228208.

[10] R. Ali and A. Haq, “A mixed GWMA-CUSUM control chart for monitoring the process mean,” Communications in Statistics-Theory and Methods, vol. 47, no. 15, pp. 3779–3801, 2018, doi: 10.1080/03610926.2017.1361994.

[11] S. L. Lu, “Novel design of composite generally weighted moving average and cumulative sum charts,” Quality and Reliability Engineering International, vol. 33, no. 8, pp. 2397–2408, 2017.

[12] R. Taboran, S. Sukparungsee, and Y. Areepong, “A new nonparametric Tukey MA – EWMA control charts for detecting mean shifts,” IEEE Access, no. 8, pp. 207249–207259, 2020.

[13] R. Taboran, S. Sukparungsee, and Y. Areepong, “Design of a new Tukey MA – DEWMA control charts for monitor process and its applications,” IEEE Access, no. 9, pp. 102746–102757, 2021.

[14] N. Saengsura, S. Sukparungsee, and Y. Areepong, “Mixed moving average-cumulative sum control chart for monitoring parameter change,” Intelligent Automation and Soft Computing, vol. 31, no. 1, pp. 635–647, 2022, doi: 10.32604/iasc.2022. 019997.

[15] M. Aslam, W. Gui, N. Khan, and C-H. Jun, “Double moving average – EWMA control chart for exponentially distributed quality,” Communications in Statistics - Simulation and Computation, vol. 46, no. 9, 2017, doi: 10.1080/03610918.2016.1236955.

[16] D. C. Montgomery, Introduction to Statistical Quality Control, 6th ed. New York: John Wiley and Sons, 2009.

[17] Organisation for Economic Co-operation and Development, “Air and GHG emissions (indicator)”, 2021, [Online]. Available: https://data.oecd.org/ air/air-and-ghg-emissions.htm

Full Text: PDF

DOI: 10.14416/j.asep.2022.12.002

Refbacks

There are currently no refbacks.

Username
Password
Remember me