IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i4p3239-d1064168.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/4/3239/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/4/3239/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Massimo Borg & Paul Refalo & Emmanuel Francalanza, 2023. "Failure Detection Techniques on the Demand Side of Smart and Sustainable Compressed Air Systems: A Systematic Review," Energies, MDPI, vol. 16(7), pages 1-36, March.
    2. Fábio de Oliveira Neves & Henrique Ewbank & José Arnaldo Frutuoso Roveda & Andrea Trianni & Fernando Pinhabel Marafão & Sandra Regina Monteiro Masalskiene Roveda, 2022. "Economic and Production-Related Implications for Industrial Energy Efficiency: A Logistic Regression Analysis on Cross-Cutting Technologies," Energies, MDPI, vol. 15(4), pages 1-19, February.
    3. Zhou, Qin & Luo, Yunzhao & Wang, Zhaojun, 2010. "A control chart based on likelihood ratio test for detecting patterned mean and variance shifts," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1634-1645, June.
    4. Chi Zhang & Fugee Tsung & Dongdong Xiang, 2016. "Monitoring censored lifetime data with a weighted‐likelihood scheme," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(8), pages 631-646, December.
    5. Guanfu Liu & Xiaolong Pu & Lei Wang & Dongdong Xiang, 2015. "CUSUM chart for detecting range shifts when monotonicity of likelihood ratio is invalid," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(8), pages 1635-1644, August.
    6. Minya Xu & Ping-Shou Zhong & Wei Wang, 2016. "Detecting Variance Change-Points for Blocked Time Series and Dependent Panel Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 213-226, April.
    7. Khanittha Talordphop & Yupaporn Areepong & Saowanit Sukparungsee, 2023. "Design and Analysis of Extended Exponentially Weighted Moving Average Signed-Rank Control Charts for Monitoring the Process Mean," Mathematics, MDPI, vol. 11(21), pages 1-15, October.
    8. Wenjuan Liang & Xiaolong Pu & Dongdong Xiang, 2017. "A distribution-free multivariate CUSUM control chart using dynamic control limits," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(11), pages 2075-2093, August.
    9. Konstantinos Salonitis, 2020. "Energy Efficiency of Manufacturing Processes and Systems—An Introduction," Energies, MDPI, vol. 13(11), pages 1-5, June.
    10. Unarine Netshiozwi & Ali Yeganeh & Sandile Charles Shongwe & Ahmad Hakimi, 2023. "Data-Driven Surveillance of Internet Usage Using a Polynomial Profile Monitoring Scheme," Mathematics, MDPI, vol. 11(17), pages 1-23, August.
    11. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
    12. Cabello Eras, Juan José & Sagastume Gutiérrez, Alexis & Sousa Santos, Vladimir & Cabello Ulloa, Mario Javier, 2020. "Energy management of compressed air systems. Assessing the production and use of compressed air in industry," Energy, Elsevier, vol. 213(C).
    13. Doner, Nimeti & Ciddi, Kerem, 2022. "Regression analysis of the operational parameters and energy-saving potential of industrial compressed air systems," Energy, Elsevier, vol. 252(C).
    14. Jaroslav Vrchota & Martin Pech & Ladislav Rolínek & Jiří Bednář, 2020. "Sustainability Outcomes of Green Processes in Relation to Industry 4.0 in Manufacturing: Systematic Review," Sustainability, MDPI, vol. 12(15), pages 1-47, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3239-:d:1064168. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.