IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i12p2025-d836640.html
   My bibliography  Save this article

An Adaptive EWMA Control Chart Based on Principal Component Method to Monitor Process Mean Vector

Author

Listed:
  • Muhammad Riaz

    (Department of Mathematics, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Babar Zaman

    (Department of Mathematics, University of Hafr Al Batin, Hafr Al Batin 39524, Saudi Arabia)

  • Ishaq Adeyanju Raji

    (Dammam Community College, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • M. Hafidz Omar

    (Department of Mathematics, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Rashid Mehmood

    (Department of Mathematics, University of Hafr Al Batin, Hafr Al Batin 39524, Saudi Arabia)

  • Nasir Abbas

    (Department of Mathematics, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

Abstract

The special causes of variations, which is also known as a shift, can occur in a single or more than one related process characteristics. Statistical process control tools such as control charts are useful to monitor shifts in the process parameters (location and/or dispersion). In real-life situation, the shift is emerging in different sizes, and it is hard to identify it with classical control charts. Moreover, more than one process of characteristics required special attention because they must monitor jointly due to the association among them. This study offers two adaptive control charts to monitor the different sizes of a shift in the process mean vector. The novelty behind this study is to use dimensionally reduction techniques such as principal component analysis (PCA) and an adaptive method such as Huber and Bi-square functions. In brief, the multivariate cumulative sum control chart based on PCA is designed, and its plotting statistic is utilized as an input in the classical exponentially weighted moving average (EWMA) control chart. The run length (RL) properties of the proposed and other control charts are obtained by designing algorithms in MATLAB through a Monte Carlo simulation. For a single shift, the performance of the control charts is assessed through an average of RL, standard deviation of RL, and standard error of RL. Likewise, overall performance measures such as extra quadratic loss, relative ARL, and the performance comparison index are also used. The comparison reveals the superiority over other control charts. Furthermore, to emphasize the application process and benefits of the proposed control charts, a real-life example of the wind turbine process is included.

Suggested Citation

  • Muhammad Riaz & Babar Zaman & Ishaq Adeyanju Raji & M. Hafidz Omar & Rashid Mehmood & Nasir Abbas, 2022. "An Adaptive EWMA Control Chart Based on Principal Component Method to Monitor Process Mean Vector," Mathematics, MDPI, vol. 10(12), pages 1-27, June.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2025-:d:836640
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/12/2025/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/12/2025/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ou, Yanjing & Wu, Zhang & Goh, Thong Ngee, 2011. "A new SPRT chart for monitoring process mean and variance," International Journal of Production Economics, Elsevier, vol. 132(2), pages 303-314, August.
    2. Jimoh Olawale Ajadi & Muhammad Riaz, 2017. "Mixed multivariate EWMA-CUSUM control charts for an improved process monitoring," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(14), pages 6980-6993, July.
    3. Luo, Yunzhao & Li, Zhonghua & Wang, Zhaojun, 2009. "Adaptive CUSUM control chart with variable sampling intervals," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2693-2701, May.
    4. Zhang Wu & Jianxin Jiao & Mei Yang & Ying Liu & Zhaojun Wang, 2009. "An enhanced adaptive CUSUM control chart," IISE Transactions, Taylor & Francis Journals, vol. 41(7), pages 642-653.
    5. Yacine Aït-Sahalia & Dacheng Xiu, 2019. "Principal Component Analysis of High-Frequency Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 287-303, January.
    6. Petros E. Maravelakis, 2012. "Measurement error effect on the CUSUM control chart," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 323-336, May.
    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. Wei Yang & Xueting Ji & Hongxing Cai & Jiujun Zhang, 2025. "Cumulative Sum Schemes for Monitoring the Ratio of Two Correlated Normal Variables in Short Production Runs with Fixed and Variable Sampling Interval Strategies: Application in Wheat Seed Processing," Mathematics, MDPI, vol. 13(4), pages 1-29, February.
    2. Lim, S.L. & Khoo, Michael B.C. & Teoh, W.L. & Xie, M., 2015. "Optimal designs of the variable sample size and sampling interval X¯ chart when process parameters are estimated," International Journal of Production Economics, Elsevier, vol. 166(C), pages 20-35.
    3. Nasir Abbas & Muhammad Riaz & Shabbir Ahmad & Muhammad Abid & Babar Zaman, 2020. "On the Efficient Monitoring of Multivariate Processes with Unknown Parameters," Mathematics, MDPI, vol. 8(5), pages 1-32, May.
    4. Ahmad, Shabbir & Riaz, Muhammad & Abbasi, Saddam Akber & Lin, Zhengyan, 2013. "On monitoring process variability under double sampling scheme," International Journal of Production Economics, Elsevier, vol. 142(2), pages 388-400.
    5. 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.
    6. Imad Khan & Muhammad Noor-ul-Amin & Dost Muhammad Khan & Salman A. AlQahtani & Mostafa Dahshan & Umair Khalil, 2023. "Monitoring of Location Parameters with a Measurement Error under the Bayesian Approach Using Ranked-Based Sampling Designs with Applications in Industrial Engineering," Sustainability, MDPI, vol. 15(8), pages 1-18, April.
    7. Bu, R. & Li, D. & Linton, O. & Wang, H., 2022. "Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data," Cambridge Working Papers in Economics 2218, Faculty of Economics, University of Cambridge.
    8. Chakraborty Ashit B. & Khurshid Anwer, 2013. "Measurement Error Effect on the Power of Control Chart for the Ratio of Two Poisson Distributions," Stochastics and Quality Control, De Gruyter, vol. 28(1), pages 15-21, October.
    9. 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.
    10. Cheng, Mingmian & Liao, Yuan & Yang, Xiye, 2023. "Uniform predictive inference for factor models with instrumental and idiosyncratic betas," Journal of Econometrics, Elsevier, vol. 237(2).
    11. Aït-Sahalia, Yacine & Kalnina, Ilze & Xiu, Dacheng, 2020. "High-frequency factor models and regressions," Journal of Econometrics, Elsevier, vol. 216(1), pages 86-105.
    12. Bollerslev, Tim & Patton, Andrew J. & Zhang, Haozhe, 2022. "Equity clusters through the lens of realized semicorrelations," Economics Letters, Elsevier, vol. 211(C).
    13. Li, Jia & Todorov, Viktor & Tauchen, George, 2016. "Inference theory for volatility functional dependencies," Journal of Econometrics, Elsevier, vol. 193(1), pages 17-34.
    14. Donggyu Kim & Minseok Shin, 2024. "Nonconvex High-Dimensional Time-Varying Coefficient Estimation for Noisy High-Frequency Observations with a Factor Structure," Working Papers 202418, University of California at Riverside, Department of Economics.
    15. Wu, Zhang & Yang, Mei & Khoo, Michael B.C. & Castagliola, Philippe, 2011. "What are the best sample sizes for the Xbar and CUSUM charts?," International Journal of Production Economics, Elsevier, vol. 131(2), pages 650-662, June.
    16. Cheng, Mingmian & Swanson, Norman R. & Yang, Xiye, 2021. "Forecasting volatility using double shrinkage methods," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 46-61.
    17. Barbara Guardabascio & Federico Brogi & Federico Benassi, 2024. "Measuring human mobility in times of trouble: an investigation of the mobility of European populations during COVID-19 using big data," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(6), pages 5181-5199, December.
    18. Ou, Yanjing & Wu, Zhang & Goh, Thong Ngee, 2011. "A new SPRT chart for monitoring process mean and variance," International Journal of Production Economics, Elsevier, vol. 132(2), pages 303-314, August.
    19. Zhang, Min & Nie, Guohua & He, Zhen, 2014. "Performance of cumulative count of conforming chart of variable sampling intervals with estimated control limits," International Journal of Production Economics, Elsevier, vol. 150(C), pages 114-124.
    20. Donggyu Kim & Minseok Shin, 2024. "Robust High-Dimensional Time-Varying Coefficient Estimation," Working Papers 202417, University of California at Riverside, Department of Economics.

    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:jmathe:v:10:y:2022:i:12:p:2025-:d:836640. 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.