An Adaptive EWMA Dispersion Control Chart Basedon Robust Estimators Under Ranked Set Sampling
Keywords:
Adaptive EWMA, Average run length, Industrial process, Robust estimators, RSSAbstract
Control charts are widely used in manufacturing and production industries to monitor and detect process variations. Traditional control charts assume that the process dispersion remains constant over time. However, in many real-world scenarios, dispersion may change over time, increasing the risk of false alarms or missed detections. To address this issue, this study improves the detection power of adaptive EWMA dispersion charts based on different robust estimators using ranked set sampling. This study constructs control limits for robust dispersion estimators using adaptive EWMA control charts. The run-length evaluation of the proposed control charts was computed using Monte Carlo simulations. The results demonstrate that dispersion-adaptive EWMA control charts based on ranked set sampling, using SD, outperform other control charts across various robust estimators. A ring-piston dataset has been used to implement the proposed study.
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[1] T. Abbas, T. Mahmood, M. Riaz, and M. Abid. Improved linear profiling methods under classical and bayesian setups: An application to chemical gas sensors. Chemometrics and Intelligent Laboratory Systems, 196:103908, 2020. 4
[2] T. Abbas, F. Rafique, T. Mahmood, and M. Riaz. Efficient phase ii monitoring methods for linear profiles under the random effect model. IEEE Access, 7:148278–148296, 2019. 1
[3] T. Abbas, M. Riaz, B. Javed, and M. Abujiya. A new scheme of dispersion charts based on neoteric ranked set sampling. Aims Math, 8(8):17996–18020, 2023. 1
[4] T. Abbas, B. Zaman, A. Atir, M. Riaz, and S. Akbar Abbasi. On improved dispersion control charts under ranked set schemes for normal and non-normal processes. Quality and Reliability Engineering International, 35(5):1313–1341, 2019. 1
[5] Z. Abbas, T. Abbas, H. Z. Nazir, and M. Riaz. A novel adaptive cusum system for efficient process mean monitoring: An application in piston ring manufacturing process. Alexandria Engineering Journal, 106:87–100, 2024. 4
[6] S. A. Abbasi. Location charts based on ranked set sampling for normal and non-normal processes. Quality and Reliability Engineering International, 35(6):1603–1620, 2019. 1
[7] S. A. Abbasi, T. Abbas, and N. A. Adegoke. Efficient cv control charts based on ranked set sampling. IEEE Access, 7:78050–78062, 2019. 4
[8] M. Abid, M. Sun, A. Shabbir, M. Bakr, and T. Abbas. An enhanced nonparametric quality control chart with application related to industrial process. Scientific Reports, 14(1):13561, 2024. 4
[9] A. Abou El-Ela, H. A. Mosalam, and R. A. Amer. Optimal control design and management of complete dc-renewable energy microgrid system. Ain Shams Engineering Journal, 14(5):101964, 2023. 1
[10] S. Akram, T. Afzal, A. Ali, et al. Construction of optimal derivative free iterative methods for nonlinear equations using lagrange interpolation. Journal of Prime Research in Mathematics, 16(1):30–45, 2020. 2.1
[11] A. Ali, T. Mahmood, H. Z. Nazir, I. Sana, N. Akhtar, S. Qamar, and M. Iqbal. Control charts for process dispersion parameter under contaminated normal environments. Quality and Reliability Engineering International, 32(7):2481–2490, 2016. 1
[12] M. Aslam, K. Khan, M. Albassam, and L. Ahmad. Moving average control chart under neutrosophic statistics, aims math. 8 (2023), 7083–7096. 4
[13] H. Baskoroputro, F. Bhatti, H. M. Humza, and A. Y. Zakiyyah. The implementation of hosoya index and hosoya polynomial into some graphs related to cycles. Journal of Prime Research in Mathematics, 20(1):15–22, 2024. 2.1
[14] T. M. Beasley, S. Erickson, and D. B. Allison. Rank-based inverse normal transformations are increasingly used, but are they merited? Behavior genetics, 39(5):580–595, 2009. 1
[15] G. Capizzi and G. Masarotto. An adaptive exponentially weighted moving average control chart. Technometrics, 45(3):199–207, 2003. 1, 2.1, 2.1
[16] Z. Chen. On ranked-set sample quantiles and their applications. Journal of Statistical Planning and Inference, 83(1):125–135, 2000. 1
[17] A. H. Elmetwaly, A. A. ElDesouky, A. I. Omar, and M. A. Saad. Operation control, energy management, and power quality enhancement for a cluster of isolated microgrids. Ain Shams Engineering Journal, 13(5):101737, 2022. 1
[18] A. Haq. A new adaptive ewma control chart using auxiliary information for monitoring the process mean. Communications in Statistics-Theory and Methods, 47(19):4840–4858, 2018. 1
[19] T. P. Hettmansperger. The ranked-set sample sign test. Journal of Nonparametric Statistics, 4(3):263–270, 1995. 3.1
[20] S. Hussain, T. Mahmood, M. Riaz, and H. Z. Nazir. A new approach to design median control charts for location monitoring. Communications in Statistics-Simulation and Computation, 51(7):3553–3577, 2022. 4
[21] A. Javed, T. Abbas, N. Abbas, and M. Riaz. Designing bayesian paradigm-based cusum scheme for monitoring shape parameter of the inverse gaussian distribution. Computers & Industrial Engineering, 192:110235, 2024. 4
[22] W. Jiang, L. Shu, and D. W. Apley. Adaptive cusum procedures with ewma-based shift estimators. Iie Transactions, 40(10):992–1003, 2008. 1
[23] I. Khan, T. Abbas, and F. R. Albogamy. Optimizing lognormal process monitoring with bayesian approach for industrial engineering applications. Quality and Reliability Engineering International, 41(4):1483–1494, 2025. 4
[24] J. Law. Robust statistics—the approach based on influence functions, 1986. 2.2.4
[25] B. MacCarthy and T. Wasusri. A review of non-standard applications of statistical process control (spc) charts. International Journal of Quality & Reliability Management, 19(3):295–320, 2002. 1
[26] D. C. Montgomery. Introduction to statistical quality control. John wiley & sons, 2020. 5
[27] H. Z. Nazir, T. Hussain, N. Akhtar, M. Abid, and M. Riaz. Robust adaptive exponentially weighted moving average control charts with applications of manufacturing processes. The International Journal of Advanced Manufacturing Technology, 105(1):733–748, 2019. 1
[28] H. Z. Nazir, M. Riaz, R. J. Does, and N. Abbas. Robust cusum control charting. Quality Engineering, 25(3):211–224, 2013. 1
[29] E. S. Page. Continuous inspection schemes. Biometrika, 41(1/2):100–115, 1954. 1
[30] Z. Rasheed, M. Khan, S. M. Anwar, M. U. Aslam, S. A. Lone, and S. A. Almutlak. Designing an efficient adaptive ewmamodel for normal process with engineering applications. Ain Shams Engineering Journal, 15(9):102904, 2024. 4
[31] Z. Raza. Leap zagreb connection numbers for some networks models. Indonesian Journal of Chemistry, 20(6):1407–1413, 2020. 1, 2.1
[32] Z. Raza and A. Ali. Bounds on the zagreb indices for molecular (n, m)-graphs. International Journal of Quantum Chemistry, 120(18):e26333, 2020. 2.1
[33] M. Riaz, T. Mahmood, S. A. Abbasi, N. Abbas, and S. Ahmad. Linear profile monitoring using ewma structure under ranked set schemes. The International Journal of Advanced Manufacturing Technology, 91(5):2751–2775, 2017. 1
[34] S. W. Roberts. Control chart tests based on geometric moving averages. Technometrics, 42(1):97–101, 2000. 1
[35] A. Shafqat, Z. Huang, and M. Aslam. Design of x-bar control chart based on inverse rayleigh distribution under repetitive group sampling. Ain Shams Engineering Journal, 12(1):943–953, 2021. 4
[36] W. A. Shewhart. Quality control charts. The Bell System Technical Journal, 5(4):593–603, 1926. 1
[37] F. Touqeer, T. Mahmood, M. Riaz, and N. Abbas. On developing linear profile methodologies: a ranked set approach with engineering application. Journal of Engineering Research, 8(2):203–225, 2020. 1
[38] E. Yashchin. Some aspects of the theory of statistical control schemes. IBM Journal of Research and Development, 31(2):199–205, 1987. 1
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Copyright (c) 2026 Tahir Abbas, Hafiz Zafar Nazir, Zohaib Husssain, Zameer Abbas, Noureen Akhtar
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