On designing a new control chart for Rayleigh distributed processes with an application to monitor glass fiber strength

References

• Abbas, N., M. Riaz, and T. Mahmood. 2017. An improved S2 control chart for cost and efficiency optimization. IEEE Access 5:19486–93. doi:10.1109/ACCESS.2017.2755440.
   Web of Science ®Google Scholar
• Abbasi, S. A. 2012. Enhancements to control charts for monitoring process dispersion and location. http://hdl.handle.net/2292/19800, https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm.
   Google Scholar
• Abbasi, S. A., and A. Miller. 2012. On proper choice of variability control chart for normal and non-normal processes. Quality and Reliability Engineering International 28 (3):279–96. doi:10.1002/qre.1244.
   Web of Science ®Google Scholar
• Adeoti, O. A., J. O. Olaomi, and K. S. Adekeye. 2016. Control chart limits for monitoring process mean based on Downton’s estimator. Quality and Reliability Engineering International 32 (5):1731–40. doi:10.1002/qre.1909.
   Web of Science ®Google Scholar
• Ali, S., and M. Riaz. 2014. Cumulative quantity control chart for the mixture of inverse Rayleigh process. Computers & Industrial Engineering 73 (1):11–20. doi:10.1016/j.cie.2014.03.021.
   Google Scholar
• Aslam, M., M. Azam, N. Khan, and C. H. Jun. 2014. A control chart for an exponential distribution using multiple dependent state sampling. Quality & Quantity 49 (2):455–62. doi:10.1007/s11135-014-0002-2.
   Google Scholar
• Chan, L. K., and H. J. Cui. 2003. Skewness correction X̄ and R charts for skewed distributions. Naval Research Logistics 50 (6):555–73. doi:10.1002/nav.10077.
   Web of Science ®Google Scholar
• Derya, K. O., and H. Canan. 2012. Control charts for skewed distributions: Weibull, gamma, and lognormal. Metodoloski Zvezki 9 (2):95–106.
   Google Scholar
• Dobson, A. J., and A. G. Barnett. 2008. An introduction to generalized linear models. 3rd ed. New York: CRC Press; Taylor & Francis Group.
   Google Scholar
• Hossain, M. P., M. H. Omar, and M. Riaz. 2017a. New V control chart for the Maxwell distribution. Journal of Statistical Computation and Simulation 87 (3):594–606. doi:10.1080/00949655.2016.1222391.
   Web of Science ®Google Scholar
• Hossain, M. P., M. H. Omar, and M. Riaz. 2017b. Estimation of mixture Maxwell parameters and its possible industrial application. Computers & Industrial Engineering 107:264–75. doi:10.1016/j.cie.2017.03.023.
   Web of Science ®Google Scholar
• Karim, R., M. P. Hossain, S. Begum, and F. Hossain. 2011. Rayleigh mixture distribution. Journal of Applied Mathematics 2011:1–17. doi:10.1155/2011/238290.
   Google Scholar
• Klugman, S. A., H. H. Panjer, and G. E. Willmot. 2012. Loss models from data to decisions. 4th ed. Hoboken, New Jersey: John Wiley and Sons, Inc.
   Google Scholar
• Li, C., N. Su, P. Su, and Y. Shyr. 2014. The design of and R control charts for skew normal distributed data. Communications in Statistics - Theory and Methods 43 (23):4908–24. doi:10.1080/03610926.2012.717666.
   Web of Science ®Google Scholar
• Li, X., and Y. Li. 2015. An analytical model for regular respiratory signals derived from the probability density function of Rayleigh distribution. In Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, MiCo - Milano Conference Center - Milan, Italy, August 25–29, 93–96. doi:10.1109/EMB2015.7318308.
   Google Scholar
• Montgomery, D. C. 2009. Introduction to statistical quality control. 6th ed. Hoboken, NJ: John Wiley & Sons, Inc.
   Google Scholar
• Mutlu, M. 2014. Application of Rayleigh probability density function in electromagnetic wave propagation. Journal of Electrical and Electronic Engineering 2 (1):17–21. doi:10.11648/j.jeee.20140201.13.
   Google Scholar
• Raza, S. M. M., M. Moeen Butt, M-u Din-Azad, and A. Faisal Siddiqi. 2016. Shewhart control charts for Rayleigh distribution in the presence of type I censored data. Journal of ISOSS 2 (2):210–7.
   Google Scholar
• Riaz, M., S. A. Abbasi, S. Ahmad, and B. Zaman. 2014. On efficient phase II process monitoring charts. Int J Adv Manuf Technol. 70 (9-12):2263–74. doi:10.1007/s00170-013-5418-7.
   Web of Science ®Google Scholar
• Roberts, S. W. 1959. Control chart tests based on geometric moving averages. Technometrics 1 (3):239–50. doi:10.1080/00401706.1959.10489860.
   Google Scholar
• Santiago, E., and J. Smith. 2013. Control charts based on the exponential distribution: Adapting runs rules for the t chart. Quality Engineering 25 (2):85–96. doi:10.1080/08982112.2012.740646.
   Web of Science ®Google Scholar
• Shewhart, W. A. 1926. Quality control charts. Bell System Technical Journal 5 (4):593–603. doi:10.1002/j.1538-7305.1926.tb00125.x.
   Google Scholar
• Smith, R. L., and J. Naylor. 1987. A comparison of maximum likelihood and Bayesian estimators for the three-parameter Weibull distribution. Appl Statist. 36 (3):358–69. doi:10.2307/2347795.
   Web of Science ®Google Scholar
• Vermaat, M. B., R. A. Ion, R. J. M. M. Does, and C. A. J. Klaassen. 2003. A comparison of Shewhart individuals control charts based on normal, non-parametric, and extreme-value theory. Quality and Reliability Engineering International 19 (4):337–53. doi:10.1002/qre.586.
   Web of Science ®Google Scholar
• Yamane, T. 1998. Application of the Rayleigh distribution to size selectivity of small prawn pots for the oriental river prawn, Macrobrachium nipponense. Fisheries Research 36 (1):27–33. doi:10.1016/S0165-7836(98)00084-8.
   Web of Science ®Google Scholar
• Yang, S., R. Zhou, and S. Lu. 2017. A median loss control chart for monitoring quality loss under skewed distributions. Journal of Statistical Computation and Simulation 87 (17):3241–20. doi:10.1080/00949655.2017.1362697.
   Web of Science ®Google Scholar
• Yang, Z., and M. Xie. 2000. Process monitoring of exponentially distributed characteristics through an optimal normalizing transformation. Journal of Applied Statistics 27 (8):1051–63. doi:10.1080/02664760050173373.
   Web of Science ®Google Scholar