Design of Nonparametric Extended Exponentially Weighted Moving Average – Sign Control Chart
Article Sidebar
Main Article Content
Abstract
The present research introduces the EEWMA-Sign chart, which incorporates the extended exponentially weighted moving average control chart with the sign control charts to detect small changes in procedures. This is a nonparametric control chart that can overcome the constraints imposed by normal assumptions. The average run lengths serve as supporting examinations for comparing the effectiveness of a monitoring scheme to the EEWMA and EWMA control charts via Monte Carlo Simulation. Besides a specific range of shift sizes, the expected ARL (EARL) remains an instrument to assess the efficiency of control charts. The overall result demonstrates that the proposed chart is the most suitable control chart for detecting small shifts between Normal, Lognormal, and Laplace distributional scenarios. Nonetheless, the EWMA chart recognizes large shifts more efficiently than others. Adapting the proposed control chart to the flow width dataset produced results consistent with the research findings.
Article Details
References
W. A. Shewhart, Economic Control of Quality Manufactured Product. New York: D. Van Nostrand Company Inc, 1930.
S. W. Roberts, “Control chart tests based on geometric moving average,” Techmometrics, vol. 1, no. 3, pp. 239–250, 1959.
E. S. Page, “Continuous inspection schemes,” Biometrika, vol. 41, no. 1–2, pp. 100–115, 1954.
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.
M. Naveed, M. Azam, N. Khan, and M. Aslam, “Design of a control chart using extended EWMA statistic,” Technologies, vol. 6, no. 4, 2018, Art. no. 108.
S. Chakraborti, M. Graham, Encyclopedia of Statistics in Quality and Reliability. New York: John Wiley and Sons, 2008.
S. F. Yang, J. S. Lin, and S. W. Cheng, “A new nonparametric EWMA sign control chart,” Expert Systems with Applications, vol. 38, no. 5, pp. 6239–6243, 2011.
S. F. Yang and S. W. Cheng, “A new nonparametric CUSUM mean chart,” Quality and Reliability Engineering International, vol. 27, no. 7, pp. 867–875, 2011.
S. L. Lu, “An extended nonparametric exponentially weighted moving average sign control chart,” Quality and Reliability Engineering International, vol. 31, no. 1, pp. 3–13, 2015.
M. Aslam, M. A. Raza, M. Azam, L. Ahmad, and C. H. Jun, “Design of a sign chart using a new EWMA statistic,” Communications in Statistics – Theory and Method, vol. 49, no. 6, pp. 1299–1310, 2020.
R. Taboran and S. Sukparungsee, “An enhanced performance to monitor process mean with modified exponentially weighted moving average- signed control chart,” Applied Science and Engineering Progress, vol. 15, no. 4, 2022, Art. no. 5532, doi: 10.14416/j.asep.2021.10.013.
K. Petcharat and S. Sukparungsee, “Development of a new MEWMA – Wilcoxon sign rank chart for detection of change in mean parameter,” Applied Science and Engineering Progress, vol. 16, no. 2, 2023, Art. no. 5892, doi: 10.14416/j.asep.2022.05.005.
H. W. You, M. K. B. Chong, C.Z. Lin, and T. W. Lin, “The expected average run length of the EWMA median chart with estimated process parameters,” Austrian Journal of Statistics, vol. 49, no. 3, pp. 19–24, 2020.
K. M. Chan, Z. L. Chong, M. B. C. Khoo, K. W. Khaw, and W. L. Teoh, “A comparative study of the EWMA and double EWMA control schemes,” Journal of Physics: Conference Series vol. 2051, 2021, Art. no. 012067.
D. C. Montgomery, Introduction to Statistical Quality Control. New York: John Wiley and Sons, 2009.