Control charts are widely used to efficiently detect small to moderate shifts and they include exponentially moving average control charts, named memory type control charts. Today, memory type control charts are a significant tool to assure quality standards and monitor manufacturing goods. The proposed study suggests a novel Bayesian exponentially weighted moving average (EWMA) control chart design utilizing various pair ranked set sampling schemes for posterior and posterior predictive distributions given an informative prior. The proposed chart strategy is evaluated in terms of the small run length characteristic by using Monte Carlo simulation methods. The comparative analysis is also carried out by using a Bayesian EWMA control chart to apply simple random sampling for the respective average and standard deviation of the run length values in the both control chart designs. The results revealed efficient and rapid detection of shifts in process means which proves the success and superiority of the suggested design. A real-life data sets is used to elaborate the efficient application of the suggested Bay-EWMA-PRSS control chart design. The overall research findings support the theoretical and simulation results, which are provided in the form of extensive tables.
Citation: Imad Khan, Muhammad Noor-ul-Amin, Narjes Turki Khalifa, Asma Arshad. EWMA control chart using Bayesian approach under paired ranked set sampling schemes: An application to reliability engineering[J]. AIMS Mathematics, 2023, 8(9): 20324-20350. doi: 10.3934/math.20231036
Control charts are widely used to efficiently detect small to moderate shifts and they include exponentially moving average control charts, named memory type control charts. Today, memory type control charts are a significant tool to assure quality standards and monitor manufacturing goods. The proposed study suggests a novel Bayesian exponentially weighted moving average (EWMA) control chart design utilizing various pair ranked set sampling schemes for posterior and posterior predictive distributions given an informative prior. The proposed chart strategy is evaluated in terms of the small run length characteristic by using Monte Carlo simulation methods. The comparative analysis is also carried out by using a Bayesian EWMA control chart to apply simple random sampling for the respective average and standard deviation of the run length values in the both control chart designs. The results revealed efficient and rapid detection of shifts in process means which proves the success and superiority of the suggested design. A real-life data sets is used to elaborate the efficient application of the suggested Bay-EWMA-PRSS control chart design. The overall research findings support the theoretical and simulation results, which are provided in the form of extensive tables.
[1] | D. C. Montgomery, Introduction to statistical quality control, John Wiley & Sons, (2007). |
[2] | W. A. Shewhart, The application of statistics as an aid in maintaining quality of a manufactured product, J. Am. Stat. Assoc., 20 (1925), 546–548. |
[3] | E. S. Page, Continuous inspection schemes, Biometrika, 41 (1954), 100–115. |
[4] | S. W. Roberts, Control chart tests based on geometric moving averages, Technometrics, 42 (2000), 97–101. |
[5] | A. L. Sweet, Control charts using coupled exponentially weighted moving averages, IIE. Trans., 18 (1986), 26–33. https://doi.org/10.1080/07408178608975326 doi: 10.1080/07408178608975326 |
[6] | C. W. Lu, M. R. R. Jr, EWMA control charts for monitoring the mean of autocorrelated processes, J. Qual. Technol., 31 (1990), 166–188. https://doi.org/10.1080/00224065.1999.11979913 doi: 10.1080/00224065.1999.11979913 |
[7] | P. E. Maravelakis, P. Castagliola, An EWMA chart for monitoring the process standard deviation when parameters are estimated, Comput. Stat. Data An., 53 (2009), 2653–2664. https://doi.org/10.1016/j.csda.2009.01.004 doi: 10.1016/j.csda.2009.01.004 |
[8] | L. Huwang, Y. H. T. Wang, A. B. Yeh, Z. S. J. Chen, On the exponentially weighted moving variance, Nav. Res. Log., 56 (2009), 659–668. https://doi.org/10.1002/nav.20369 doi: 10.1002/nav.20369 |
[9] | S. H. Altoum, H. A. Othman, H. Rguigui, Quantum white noise Gaussian kernel operators, Chaos, Soliton. Fract., 104 (2017), 468–476. https://doi.org/10.1016/j.chaos.2017.08.039 doi: 10.1016/j.chaos.2017.08.039 |
[10] | S. Noor, M. Noor-ul-Amin, M. Mohsin, A. Ahmed, Hybrid exponentially weighted moving average control chart using Bayesian approach, Commun. Stat.-Theor. M., 51 (2020), 1–25. https://doi.org/10.1080/03610926.2020.1805765 doi: 10.1080/03610926.2020.1805765 |
[11] | A. B. Makhlouf, M. Lassaad, H. A. Othman, H. M. S. Rguigui, S. Boulaaras, Proportional Itô–Doob stochastic fractional order systems, Mathematics, 11 (2023), 2049. https://doi.org/10.3390/math11092049 doi: 10.3390/math11092049 |
[12] | U. Menzefricke, On the evaluation of control chart limits based on predictive distributions, Commun. Stat.-Theor. M., 31 (2002), 1423–1440. https://doi.org/10.1081/STA-120006077 doi: 10.1081/STA-120006077 |
[13] | U. Menzefricke, Control charts for the generalized variance based on its predictive distribution, Commun. Stat.-Theor. M., 36 (2007), 1031–1038. https://doi.org/10.1080/03610920601036176 doi: 10.1080/03610920601036176 |
[14] | K. L. Tsui, W. H. Woodall, Multivariate control charts based on loss functions, Sequential Anal., 12 (1993), 79–92. https://doi.org/10.1080/07474949308836270 doi: 10.1080/07474949308836270 |
[15] | Z. Wu, Y. Tian, Weighted-loss-function CUSUM chart for monitoring mean and variance of a production process, Int. J. Prod. Res., 43 (2005), 3027–3044. https://doi.org/10.1080/00207540500057639 doi: 10.1080/00207540500057639 |
[16] | M. Elghribi, H. A. Othman, Al-H. A. Al-Nashri, Homogeneous functions: New characterization and applications, T. A. Razmadze Math. In., 171 (2017), 171–181. https://doi.org/10.1016/j.trmi.2016.12.006 doi: 10.1016/j.trmi.2016.12.006 |
[17] | S. Riaz, M. Riaz, A. Nazeer, Z. Hussain, On Bayesian EWMA control charts under different loss functions, Qual. Reliab. Eng. Int., 33 (2017), 2653–2665. https://doi.org/10.1002/qre.2224 doi: 10.1002/qre.2224 |
[18] | S. Noor, M. Noor-ul-Amin, M. Mohsin, A. Ahmed, Hybrid exponentially weighted moving average control chart using Bayesian approach, Commu. Stat. Theor. M., 51 (2020), 1–25. https://doi.org/10.1080/03610926.2020.1805765 doi: 10.1080/03610926.2020.1805765 |
[19] | M. Noor‐ul‐Amin, S. Noor, An adaptive EWMA control chart for monitoring the process mean in Bayesian theory under different loss functions, Qual. Reli. Eng. Int., 37 (2021), 804–819. https://doi.org/10.1002/qre.2764 doi: 10.1002/qre.2764 |
[20] | H. Jeffreys, An invariant form for the prior probability in estimation problems, Proc. Roy. Soc. Lond. Seri. A. Math. Phys. Sci., 186 (1946), 453–461. https://doi.org/10.1098/rspa.1946.0056 doi: 10.1098/rspa.1946.0056 |
[21] | C. Gauss, Method des Moindres Carres Memoire sur la Combination des Observations, 1810. Trans. J. Bertrand. South Carolina: Nabu Press, 1955. |
[22] | H. R. Varian, A Bayesian approach to real estate assessment, Studies in Bayesian econometric and statistics in Honor of Leonard J. Savage, 1975,195–208. |
[23] | M. Hu, Median ranked set sampling. J. Appl. Stat. Sci., 6 (1997), 245–255. |
[24] | S. Balci, A. D. Akkaya, B. E. Ulgen, Modified maximum likelihood estimators using ranked set sampling, J. Comput. Appl. Math., 238 (2013), 171–179. https://doi.org/10.1016/j.cam.2012.08.030 doi: 10.1016/j.cam.2012.08.030 |
[25] | M. Tayyab, M. Noor-ul-Amin, M. Hanif, Exponential weighted moving average control charts for monitoring the process mean using pair ranked set sampling schemes, Iran. J. Sci. Technol., 43 (2019), 1941–1950. https://doi.org/10.1007/s40995-018-0668-8 doi: 10.1007/s40995-018-0668-8 |