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Mixed Exponentially Weighted Moving Average—Moving Average Control Chart with Application to Combined Cycle Power Plant

Author

Listed:
  • Muhammad Ali Raza

    (Department of Statistics, Government College University Faisalabad, Faisalabad 38000, Pakistan)

  • Komal Iqbal

    (Department of Statistics, Government College University Faisalabad, Faisalabad 38000, Pakistan)

  • Muhammad Aslam

    (Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia)

  • Tahir Nawaz

    (Department of Statistics, Government College University Faisalabad, Faisalabad 38000, Pakistan)

  • Sajjad Haider Bhatti

    (College of Statistical Sciences, University of the Punjab, Lahore 54590, Pakistan)

  • Gideon Mensah Engmann

    (Department of Biometry, C. K. Tedam University of Technology and Applied Sciences, Navrongo P.O. Box 24, Ghana)

Abstract

Statistical process control (SPC) consists of various tools for effective monitoring of the production processes and services to ensure their stable and satisfactory performance. A control chart is an important tool of SPC for detecting the process shifts that may undermine the quality of the products or services. In the literature, a mixed exponentially weighted moving average–moving average (EWMA–MA) control chart for monitoring the process location is proposed to enhance the overall shift detection ability of the EWMA control chart. It is noted that the moving averages terms were considered as independent irrespective of their order. Consequently, the covariance terms are ignored while deriving the variance expression of the monitoring statistic. However, the successive moving averages of span w might not be independent since each term includes w − 1 preceding samples’ information. In this study, the variance expression of the mixed EWMA-MA charting statistic is derived by considering the dependency among the sequential moving averages. The control limits of the mixed EWMA-MA control chart are revised and the run-length profile is studied by using Monte Carlo simulations. The performance of the mixed EWMA-MA chart is compared with the existing counterparts and its robustness under various process distributions is studied. In the end, a real-life example is provided to demonstrate its application by using the data from a combined cycle power plant.

Suggested Citation

  • Muhammad Ali Raza & Komal Iqbal & Muhammad Aslam & Tahir Nawaz & Sajjad Haider Bhatti & Gideon Mensah Engmann, 2023. "Mixed Exponentially Weighted Moving Average—Moving Average Control Chart with Application to Combined Cycle Power Plant," Sustainability, MDPI, vol. 15(4), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3239-:d:1064168
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    References listed on IDEAS

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    1. Han, Dong & Tsung, Fugee, 2006. "A Reference-Free Cuscore Chart for Dynamic Mean Change Detection and a Unified Framework for Charting Performance Comparison," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 368-386, March.
    2. Zahid Rasheed & Hongying Zhang & Muhammad Arslan & Babar Zaman & Syed Masroor Anwar & Muhammad Abid & Saddam Akber Abbasi, 2021. "An Efficient Robust Nonparametric Triple EWMA Wilcoxon Signed-Rank Control Chart for Process Location," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-28, October.
    3. Miriam Benedetti & Francesca Bonfà & Vito Introna & Annalisa Santolamazza & Stefano Ubertini, 2019. "Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications," Energies, MDPI, vol. 12(20), pages 1-28, October.
    4. Saowanit Sukparungsee & Yupaporn Areepong & Rattikarn Taboran, 2020. "Exponentially weighted moving average—Moving average charts for monitoring the process mean," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-24, February.
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    1. Liu, Zhi-Feng & Liu, You-Yuan & Chen, Xiao-Rui & Zhang, Shu-Rui & Luo, Xing-Fu & Li, Ling-Ling & Yang, Yi-Zhou & You, Guo-Dong, 2024. "A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting," Applied Energy, Elsevier, vol. 360(C).

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