Detecting special-cause variation ‘events’ from process data signatures

References

• Z.G. Stoumbos, M.R. Reynolds Jr, T.P. Ryan, and W.H. Woodall, The state of statistical process control as we proceed into the 21st Century. J. Am. Stat. Assoc. 95 (2000), pp. 992–998. doi: https://doi.org/10.1080/01621459.2000.10474292
   Web of Science ®Google Scholar
• D.P. Bischak, D. Trietsch, The rate of false signals in control charts with estimated limits. J. Qual. Technol. 39 (2007), pp. 54–65. doi: https://doi.org/10.1080/00224065.2007.11917673
   Web of Science ®Google Scholar
• W. Woodall, H. Spitzner, D.J. Montgomery, et al., Using control charts to monitor process and product quality profiles. J. Qual. Technol. 36 (2004), pp. 309–320. doi: https://doi.org/10.1080/00224065.2004.11980276
   Web of Science ®Google Scholar
• T.M. Rhyne, L. Treinish, Data signatures and visualization of scientific data sets. IEEE Comput Graph 3/4 (2000), pp. 12–15.
   Google Scholar
• W.H. Woodall, Controversies and contradictions in statistical process control. J. Qual. Technol. 32 (2000), pp. 341–350. doi: https://doi.org/10.1080/00224065.2000.11980013
   Web of Science ®Google Scholar
• Y. Liao, F. Deschamps, E. de Freitas, et al., Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal. Int. J. Prod. Res. 55 (2017), pp. 3609–3629. doi: https://doi.org/10.1080/00207543.2017.1308576
   Web of Science ®Google Scholar
• W.E. Deming, On probability as a basis for action. Am. Stat. 29 (1975), pp. 146–152.
   Web of Science ®Google Scholar
• W.E. Deming, Out of the Crisis, Massachusetts Institute of Technology, Center for Advanced Engineering Study, Cambridge, MA, 1986.
   Google Scholar
• G. Taguchi, Taguchi on Robust Technology Development, The American Society of Mechanical Engineers, American Society of Mechanical Engineers (ASME) Press, New York, NY, 1993.
   Google Scholar
• D.J. Edwards, R.V. León, T.M. Young, F.M. Guess, and K.A. Crookston, Comparison of two wood plastic composite extruders using bootstrap confidence intervals on measurements of sample failure data. Qual. Eng. 25 (2013), pp. 23–33. doi: https://doi.org/10.1080/08982112.2012.728496
   Google Scholar
• J.F. Lawless, R.J. Mackay, J.A. Robinson, Analysis of variation transmission in manufacturing processes – part I. J. Qual. Technol. 31 (1999), pp. 131–142. doi: https://doi.org/10.1080/00224065.1999.11979910
   Web of Science ®Google Scholar
• G.J. Hahn, Deming’s impact on industrial statistics: some reflections. Am. Stat. 49 (1995), pp. 336–341.
   Web of Science ®Google Scholar
• M.R. Reynolds Jr, Z.G. Stoumbos, Monitoring the process mean and variance using individual observations and variable sampling intervals. J. Qual. Technol. 33 (2000), pp. 181–205. doi: https://doi.org/10.1080/00224065.2001.11980066
   Google Scholar
• G.J. Hahn, W.Q. Meeker, Statistical Intervals, John Wiley and Sons, New York, NY, 1991.
   Google Scholar
• W. Shewhart, Economic Control of Quality of Manufactured Product, D. Van Nostrand Company, New York, NY, 1931.
   Google Scholar
• T.M. Young, N.E. Clapp Jr, F.M. Guess, and C.-H. Chen, Predicting key reliability response with limited response data. Qual. Eng. 26 (2014), pp. 223–232. doi: https://doi.org/10.1080/08982112.2013.807930
   Web of Science ®Google Scholar
• A. Ceriolo, F. Laurini, and A. Corbellini, Functional cluster analysis of financial time series, Proc. of the Classification and Data Analysis Group of the Italian Statistical Society, September 22–24, Bologna, Italy, 2003.
   Google Scholar
• J.S. Morris, C. Arroyo, B.A. Coull, L.M. Ryan, R. Herrick, and S.L. Gortmaker, Using wavelet-based functional mixed models to characterize population heterogeneity in accelerator profiles: a case study. J. Am. Stat. Assoc. 101 (2006), pp. 1352–1364. doi: https://doi.org/10.1198/016214506000000465
   PubMed Web of Science ®Google Scholar
• E. Bertran, M. Blanco, S. Maspoch, M.C. Ortiz, M.S. Sanchez, and L.A. Sarabia, Handling intrinsic non-linearity in near-infrared reflectance spectroscopy. Chemomet Intell. Lab. 49 (1999), pp. 215–224. doi: https://doi.org/10.1016/S0169-7439(99)00043-X
   Web of Science ®Google Scholar
• A. Bowman, S. Young, Graphical comparison of nonparametric curves. Appl. Stat. 45 (1996), pp. 83–98. doi: https://doi.org/10.2307/2986225
   Web of Science ®Google Scholar
• B.M. Colosimo, Q. Semeraro, M. Pacella, Statistical process control for geometric specifications: on monitoring of roundness profiles. J. Qual. Technol. 40 (2008), pp. 1–18. doi: https://doi.org/10.1080/00224065.2008.11917709
   Web of Science ®Google Scholar
• S. Golowich, J. Landwehr, S. Vander Wirl, Interplay between physics and statistics for modeling optical fiber bandwidth. Technometrics. 44 (2002), pp. 215–229. doi: https://doi.org/10.1198/004017002188618400
   Web of Science ®Google Scholar
• N. Heckman, R.H. Zamar, Comparing shapes of regression functions. Biometrika 87 (2000), pp. 135–144. doi: https://doi.org/10.1093/biomet/87.1.135
   Web of Science ®Google Scholar
• K. Kim, M.A. Mahmoud, W.H. Woodall, On the monitoring of linear profiles. J. Qual. Technol. 35 (2003), pp. 317–328. doi: https://doi.org/10.1080/00224065.2003.11980225
   Web of Science ®Google Scholar
• M.S. Sanchez, E. Bertran, L.A. Sarabia, M.C. Ortiz, M. Blanco, and J. Coello, Quality control decisions with near infrared data. Chemometr. Intell. Lab. 53 (2000), pp. 69–80. doi: https://doi.org/10.1016/S0169-7439(00)00094-0
   Google Scholar
• T.G. Rials, S.S. Kelley, C.-L. So, Use of advanced spectroscopy techniques for predicting the mechanical properties of wood composites. Wood Fiber Sci. 34 (2002), pp. 398–407.
   Web of Science ®Google Scholar
• C.-D. Lai, D.N. Murthy, M. Xie, Weibull distributions and their applications, in Springer Handbook of Engineering Statistics, Pham H., ed., Springer Handbooks, Springer, London, 2006.
   Google Scholar
• W. Hardle, Applied Nonparametric Regression, Cambridge University Press, New York, NY, 1990.
   Google Scholar
• J.S. Milton, J.C. Arnold, Introduction to Probability and Statistics – Principles and Applications for Engineering and the Computing Sciences, Fourth Ed, McGraw Hill, Inc., Boston, MA, 2003.
   Google Scholar
• N. Fisher, Graphical methods in statistics: current and prospective views, Proc. of the 46th ISI Session 3, 1987.
   Google Scholar
• G. Wahba, Bayesian confidence intervals for the cross-validated smoothing spline. J. R. Stat. Soc. 45 (1983), pp. 133–150.
   Google Scholar
• Y. Wang, G. Wahba, Bootstrap confidence intervals for smoothing splines and their comparison to Bayesian confidence intervals. J. Stat. Comput. Sim. 51 (1995), pp. 263–279. doi: https://doi.org/10.1080/00949659508811637
   Web of Science ®Google Scholar
• K.E. Vokovinsky, L.B. Pfahler, The role of normal data distribution in pharmaceutical development and manufacturing pharmaceutical technology. Pharm. Tech. 38 (2014), pp. 1–3.
   Google Scholar