Batch process control and monitoring: a Dual STATIS and Parallel Coordinates (DS-PC) approach

References

• Abdi, H., Williams, L. J., Valentin, D., & Bennani-Dosse, M. (2012). STATIS and DISTATIS: Optimum multitable principal component analysis and three way metric multidimensional scaling. Wiley Interdisciplinary Reviews: Computational Statistics, 4(2), 124–167.
   Google Scholar
• Albazzaz, H. (2004). Statistical process control charts for batch operations based on independent component analysis. Industrial & Engineering Chemistry Research, 43(21), 6731–6741.
   Web of Science ®Google Scholar
• Bersimis, S., Psarakis, S., & Panaretos, J. (2007). Multivariate statistical process control charts: An overview. Quality and Reliability Engineering International, 23(5), 517–543.
   Web of Science ®Google Scholar
• Birol, G., Ündey, C., & Çinar, A. (2002). A modular simulation package for fedbatch fermentation: Penicillin production. Computers & Chemical Engineering, 26(11), 1553–1565.
   Web of Science ®Google Scholar
• Comfort, J. R., Warner, T. R., Vargo, E. P., & Bass, E. J. (2011). Parallel coordinates plotting as a method in process control hazard identification. Proceedings of the 2011 IEEE Systems and Information Engineering Design Symposium (pp. 152–157). Charlottesville, VA, USA: University of Virginia.
   Google Scholar
• Doan, X.-T., & Srinivasan, R. (2008). Online monitoring of multi-phase batch processes using phase-based multivariate statistical process control. Computers & Chemical Engineering, 32(1–2), 230–243.
   Web of Science ®Google Scholar
• Donoho, D. L., & Gasko, M. (1992). Breakdown properties of location estimates based on halfspace depth and projected outlyingness. The Annals of Statistics, 20(4), 1803–1827.
   Web of Science ®Google Scholar
• Dunia, R., Edgar, T., & Nixon, M. (2012). Process monitoring using principal components in parallel coordinates. American Institute of Chemical Engineers Journal, 59(2), 1–12.
   Web of Science ®Google Scholar
• Escoufier, Y. (1987). Three-mode data analysis: The STATIS method. In B. Fichet & C. Lauro (Eds.), Methods for multidimensional data analysis (pp. 259–272). Napoli: ECAS.
   Google Scholar
• Filho, D., & Luna, L. (2015). Multivariate quality control of batch processes using STATIS. The International Journal of Advanced Manufacturing Technology, 82(5), 867–875.
   Web of Science ®Google Scholar
• Flores-Cerrillo, J., & MacGregor, J. F. (2002). Control of particle size distributions in emulsion semibatch polymerization using mid-course correction policies. Industrial and Engineering Chemistry Research, 41(7), 1805–1814.
   Web of Science ®Google Scholar
• Fogliatto, F. S., & Niang, N. (2009). Multivariate statistical control of batch processes with variable duration. IEEE International Conference on Industrial Engineering and Engineering Management (pp. 434–438). Hong Kong, China: IEEE.
   Google Scholar
• Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58(3), 453–467.
   Web of Science ®Google Scholar
• Galindo, P. (1986). Una alternativa de representación simultánea: HJ-Biplot. Qüestiió: Quaderns d’Estadística, Sistemes, Informatica I Investigació Operativa, 10(1), 13–23.
   Google Scholar
• Golub, G., & Van Loan, C. (1996). Matrix computations (3rd ed.). Baltimore & London: Johns Hopkins University Press.
   Google Scholar
• Gower, J., Gardner-Lubbe, S., & le Roux, N. (2011). Understanding biplots. UK: John Wiley & Sons.
   Google Scholar
• Hanyuan, Z., & Xuemin, T. (2017). Batch process monitoring based on batch dynamic Kernel slow feature analysis. Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017, 5 (pp. 4772–4777). Chongqing, China: IEEE.
   Google Scholar
• Harris, T. C., Seppala, C. T., & Desborough, L. D. (1999). A review of performance monitoring and assessment techniques for univariate and multivariate control systems. Journal of Process Control, 9(1), 1–17.
   Web of Science ®Google Scholar
• Heinrich, J., & Weiskopf, D. (2013). State of the art of parallel coordinates. In Eurographics Conference on Visualization (Eurovis) (pp. 95–116). Leipzig, Germany: IEEE.
   Google Scholar
• Horn, R., & Johnson, C. (2013). Matrix analysis (2nd ed.). New York, NY, USA: Cambridge University Press.
   Google Scholar
• Hotelling, H. (1947). Multivariate quality control illustrated by air testing of sample bombsights. In C. Eisenhart, M. W. Hastay, & W. A. Wallis (Eds.), Techniques of statistical analysis (pp. 111–184). New York, NY: McGraw Hill.
   Google Scholar
• Inselberg, A. (2009). Parallel coordinates: Intelligent multidimensional visualization (D. Plemenos & G. Miaoulis, Eds.). Berlin, Heidelberg: Springer.
   Google Scholar
• Inselberg, A., & Dimsdale, B. (1990). Parallel coordinates: A tool for visualizing multi-dimensional geometry. Proceedings of the First IEEE Conference on Visualization (pp. 361–378). San Francisco, CA, USA: IEEE.
   Google Scholar
• Jackson, J. E. (1991). A User’s Guide to Principal Components. (Vol. 26). USA: John Wiley & Sons.
   Google Scholar
• Jackson, J. E., & Mudholkar, G. S. (1979). Control procedures for residuals associated with principal component analysis. Technometrics, 21(3), 341–349.
   Web of Science ®Google Scholar
• Kourti, T. (2003). Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. Journal of Chemometrics, 17(1), 93–109.
   Web of Science ®Google Scholar
• Kourti, T., & MacGregor, J. F. (1996). Multivariate SPC methods for process and product monitoring. Journal of Quality Technology, 28(4), 409–428.
   Web of Science ®Google Scholar
• Kourti, T., Nomikos, P., & MacGregor, J. F. (1995). Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS. Journal of Process Control, 5(4), 277–284.
   Web of Science ®Google Scholar
• Kruppa, J., & Jung, K. (2017). Automated multigroup outlier identification in molecular high-throughput data using bagplots and gemplots. BMC Bioinformatics, 18(232), 1–10.
   PubMedGoogle Scholar
• L'Hermier des Plantes, H. (1976). Structuration des Tableaux à Trois Indices de la Statistique: Théorie et Application d’une Méthode d’ Analyse Conjointe. Université des Sciences et Techniques du Languedoc, Montpellier.
   Google Scholar
• Lange, B., Rodriguez, N., Puech, W., & Vasques, X. (2011). Visualization assisted by parallel processing. Parallel Processing for Imaging Applications. San Francisco, CA, USA: SPIE.
   Google Scholar
• Lavit, C., Escoufier, Y., Sabatier, R., & Traissac, P. (1994). The ACT (STATIS method). Computational Statistics & Data Analysis, 18(1), 97–119.
   Web of Science ®Google Scholar
• Lee, J. M., Yoo, C. K., & Lee, I. B. (2004). Fault detection of batch processes using multiway kernel principal component analysis. Computers and Chemical Engineering, 28(9), 1837–1847.
   Web of Science ®Google Scholar
• Lewis, D. (2014). Control charts for batch processes. Wiley StatsRef: Statistics Reference Online, 1–9.  Lake Oswego, OR, USA: John Wiley & Sons.
   Google Scholar
• Liu, R. Y. (1995). Control charts for multivariate processes. Journal of the American Statistical Association, 90(432), 1380–1387.
   Web of Science ®Google Scholar
• Liu, R. Y., & Tang, J. (1996). Control charts for dependent and independent measurements based on bootstrap methods. Journal of the American Statistical Association, 91(436), 1694–1700.
   Web of Science ®Google Scholar
• Lombardo, R., Vanacore, A., & Durand, J. (2008). Non parametric control chart by multivariate additive partial least squares via spline. In C. Preisach, H. Burkhardt, L. Schmidt-Thieme, & R. Decker (Eds.), Data analysis, machine learning and applications: Proceedings of the 31st annual conference of the Gesellschaft für Klassifikation (pp. 201–208). Berlin Heidelberg: Springer-Verlag.
   Google Scholar
• Lowry, C. A., & Montgomery, D. C. (1995). A review of multivariate control charts. IIE Transactions, 27(6), 800–810.
   Web of Science ®Google Scholar
• Macgregor, J. F. (1997). Using on-line process data to improve quality: Challenges for statisticians. International Statistical Review, 65(3), 309–323.
   Web of Science ®Google Scholar
• Montgomery, D. (2009). Introduction to statistical quality control. Hoboken, NJ, USA: John Wiley & Sons.
   Google Scholar
• Næs, T., Brockhoff, P. B., & Tomic, O. (2010). Statistics for sensory and consumer science. London: John Wiley & Sons.
   Google Scholar
• Narayanachar, P. (2013). R statistical application development by example beginner’s guide. UK: Packt Publishing Ltd.
   Google Scholar
• Niang, N., Fogliatto, F., & Saporta, G. (2009). Batch process monitoring by three-way data analysis approach. The XIIIth International Conference on Applied Stochastic Models and Data Analysis (pp. 463–468). Vilnius, Lithuania: ASMDA International.
   Google Scholar
• Niang, N., Fogliatto, F. S., & Saporta, G. (2013). Non parametric on-line control of batch processes based on STATIS and clustering. Journal de La Société Francaise de Statistique, 154(3), 124–142.
   Google Scholar
• Nomikos, P., & MacGregor, J. F. (1995). Multivariate PSC charts for monitoring batch processes. Technometrics, 37, 41–59.
   Web of Science ®Google Scholar
• Rousseeuw, P. J., Ruts, I., & Tukey, J. W. (1999). The bagplot: A bivariate boxplot. American Statistician, 53(4), 382–387.
   Web of Science ®Google Scholar
• Scepi, G. (2002). Parametric and non parametric multivariate quality control charts. In C. Lauro, J. Antoch, V. E. Vinzi, & G. Saporta (Eds.), Multivariate total quality control: Foundation and recent advances (pp. 163–189). Heidelberg: Physica-Verlag HD.
   Google Scholar
• Trefethen, L., & Bau, D. (1997). Numerical linear algebra (1st ed.). Philadelphia: Society for Industrial and Applied Mathematics.
   Google Scholar
• Tucker, L. R. (1964). The extension of factor analysis to three-dimensional matrices. In H. Gulliksen & N. Frederiksen (Eds.), Contributions to mathematical psychology (pp. 110–182). New York, NY: Holt, Rinehart & Winston.
   Google Scholar
• Wang, X., Medasani, S., Marhoon, F., & Albazzaz, H. (2004). Multidimensional visualization of principal component scores for process historical data analysis. Industrial & Engineering Chemistry Research, 43(22), 7036–7048.
   Web of Science ®Google Scholar
• Wegman, E. (1990). Hyperdimensional data analysis using parallel coordinates. Journal of the American Statistical Association, 85(411), 664–675.
   Web of Science ®Google Scholar
• Westerhuis, J. A., Kourti, T., & MacGregor, J. F. (1998). Analysis of multiblock and hierarchical PCA and PLS models. Journal of Chemometrics, 12, 301–321.
   Web of Science ®Google Scholar
• Wierda, S. J. (1994). Multivariate Statistical Process Control - recent results and directions for future research. Statistica Neerlandica, 48(2), 147–168.
   Google Scholar
• Yoo, C. K., Lee, J. M., Vanrolleghem, P. A., & Lee, I. B. (2004). On-line monitoring of batch processes using multiway independent component analysis. Chemometrics and Intelligent Laboratory Systems, 71(2), 151–163.
   Web of Science ®Google Scholar
• Zani, S., Riani, M., & Corbellini, A. (1998). Robust bivariate boxplots and multiple outlier detection. Computational Statistics and Data Analysis, 28(3), 257–270.
   Web of Science ®Google Scholar