Multivariate statistical process control methods for batch production: a review focused on applications

References

• Abdi, H. (2010). Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley interdisciplinary reviews. Computational statistics, 2(1), 97–106. https://doi.org/https://doi.org/10.1002/wics.51
   Google Scholar
• Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley interdisciplinary reviews. Computational statistics, 2(4), 433–459. https://doi.org/https://doi.org/10.1002/wics.101
   Google Scholar
• 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. https://doi.org/https://doi.org/10.1002/wics.198
   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. https://doi.org/https://doi.org/10.1021/ie049582+
   Web of Science ®Google Scholar
• Bellman, R. (1997). Introduction to matrix analysis (2nd ed. ; ed., R. E. O’Malley.). Classics in Applied Mathematics.
   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. https://doi.org/https://doi.org/10.1002/qre.829
   Web of Science ®Google Scholar
• Carroll, J. D., & Chang, -J.-J. (1970). Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition. Psychometrika, 35(3), 283–319. https://doi.org/https://doi.org/10.1007/BF02310791
   Web of Science ®Google Scholar
• Chen, J., & Liu, J. (2000). Post analysis on different operating time processes using orthonormal function approximation and multiway principal component analysis. Journal of process control, 10(5), 411–418. https://doi.org/https://doi.org/10.1016/S0959-1524(00)00016-0
   Web of Science ®Google Scholar
• Cho, H. W., & Kim, K. J. (2018). A method for predicting future observations in the monitoring of a batch process. Journal of Quality Technology, 35(1), 59–69. https://doi.org/https://doi.org/10.1080/00224065.2003.11980191
   Google Scholar
• Cho, H. W., Kim, K. J., & Jeong, M. K. (2006). Online monitoring and diagnosis of batch processes: Empirical model-based framework and a case study. International Journal of Production Research, 44(12), 2361–2378. https://doi.org/https://doi.org/10.1080/00207540500422130
   Web of Science ®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. https://doi.org/https://doi.org/10.1016/j.compchemeng.2007.05.010
   Web of Science ®Google Scholar
• Dong, D., & McAvoy, T. J. (1996). Batch tracking via nonlinear principal component analysis. AIChE Journal, 42(8), 2199–2208. https://doi.org/https://doi.org/10.1002/aic.690420810
   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. https://doi.org/https://doi.org/10.1002/aic.13846
   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). ECAS.
   Google Scholar
• Ferreira, A. P., Lopes, J. A., & Menezes, J. C. (2007). Study of the application of multiway multivariate techniques to model data from an industrial fermentation process. Analytica chimica acta, 595(1–2), 120–127. https://doi.org/https://doi.org/10.1016/j.aca.2007.05.007
   PubMed Web of Science ®Google Scholar
• Filho, D. M., & Luna, L. P. (2015). Multivariate quality control of batch processes using STATIS. International journal of advanced manufacturing technology, 82(5–8), 867–875. https://doi.org/https://doi.org/10.1007/s00170-015-7428-0
   Web of Science ®Google Scholar
• Flores-Cerrillo, J., & MacGregor, J. F. (2005). Iterative learning control for final batch product quality using partial least squares models. Industrial & engineering chemistry research, 44(24), 9146–9155. https://doi.org/https://doi.org/10.1021/ie048811p
   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, 434–438. Hong Kong, China. https://doi.org/https://doi.org/10.1109/IEEM.2009.5373316
   Google Scholar
• Gallagher, N. B., Wise, B. M., & Stewart, C. W. (1996). Application of multi-way principal components analysis to nuclear waste storage tank monitoring. Computers & chemical engineering, 20(96), S739–S744. https://doi.org/https://doi.org/10.1016/0098-1354(96)00131-7
   Google Scholar
• García-Muñoz, S., Kourti, T., & MacGregor, J. F. (2004). Model predictive monitoring for batch processes. Industrial & engineering chemistry research, 43(18), 5929–5941. https://doi.org/https://doi.org/10.1021/ie034020w
   Web of Science ®Google Scholar
• Geladi, P., Manley, M., & Lestander, T. (2003). Scatter plotting in multivariate data analysis. Journal of chemometrics, 17(8–9), 503–511. https://doi.org/https://doi.org/10.1002/cem.814
   Web of Science ®Google Scholar
• Gourvénec, S., Stanimirova, I., Saby, C. A., Airiau, C. Y., & Massart, D. L. (2005). Monitoring batch processes with the STATIS approach. Journal of chemometrics, 19(5–7), 288–300. https://doi.org/https://doi.org/10.1002/cem.931
   Web of Science ®Google Scholar
• Harshman, R. A. (1970). Foundations of the PARAFAC procedure: Models and conditions for an “explanatory” multimodal factor analysis. UCLA Working Papers in Phonetics, 16(10), 1–84. https://www.psychology.uwo.ca/faculty/harshman/wpppfac0.pdf
   Google Scholar
• Heinrich, J., & Weiskopf, D. (2013). State of the art of parallel coordinates. Eurographics Conference on Visualization (EuroVis), 95–116. Leipzig, Germany. https://doi.org/https://doi.org/10.2312/conf/EG2013/stars/095-116
   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). McGraw Hill.
   Google Scholar
• Hyvärinen, A., & Oja, E. (2000). Independent component analysis: Algorithms and applications. Neural Networks, 13(4–5), 411–430. https://doi.org/https://doi.org/10.1016/S0893-6080(00)00026-5
   PubMed Web of Science ®Google Scholar
• Inselberg, A. (2009). Parallel coordinates: Intelligent multidimensional visualization (D. Plemenos & G. Miaoulis, eds.)). 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, San Francisco, CA, USA. 361–378. http://dl.acm.org/citation.cfm?id=949531.949588
   Google Scholar
• Jiang, Q., Gao, F., Yi, H., & Yan, X. (2018). Multivariate statistical monitoring of key operation units of batch processes based on time-slice CCA. IEEE Transactions on Control Systems Technology, 27(3), 1368–1375. https://doi.org/https://doi.org/10.1109/TCST.2018.2803071
   Web of Science ®Google Scholar
• Kiers, H. A. L., & Krijnen, W. P. (1991). An efficient algorithm for PARAFAC of three-way data with large numbers of observation units. Psychometrika, 56(1), 147–152. https://doi.org/https://doi.org/10.1007/BF02294592
   Web of Science ®Google Scholar
• Kosanovich, K. A., Dahl, K. S., & Piovoso, M. J. (1996). Improved process understanding using multiway principal component analysis. Industrial & engineering chemistry research, 35(1), 138–146. https://doi.org/https://doi.org/10.1021/ie9502594
   Web of Science ®Google Scholar
• Kosanovich, K. A., Piovoso, M. J., Dahl, K. S., MacGregor, J. F., & Nomikos, P. (1994). Multi-way PCA applied to an industrial batch process. American Control Conference, Baltimore, MD, USA. 1294–1298. https://doi.org/https://doi.org/10.1109/acc.1994.752268
   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. https://doi.org/https://doi.org/10.1016/0959-1524(95)00019-M
   Web of Science ®Google 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.
   Google Scholar
• Lavit, C., Escoufier, Y., Sabatier, R., & Traissac, P. (1994). The ACT (STATIS method). Computational statistics & data analysis, 18(1), 97–119. https://doi.org/https://doi.org/10.1016/0167-9473(94)90134-1
   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 & chemical engineering, 28(9), 1837–1847. https://doi.org/https://doi.org/10.1016/j.compchemeng.2004.02.036
   Web of Science ®Google Scholar
• Lewis, D. (2014). Control charts for batch processes. In Wiley StatsRef: Statistics reference online (pp. 1–9). John Wiley & Sons.
   Google Scholar
• Lin, K., & Chen, J. (2008). Two-step MPLS-based iterative learning control for batch processes. International MultiConference of Engineers and Computer Scientists, 2169(1), 1298–1303. https://pdfs.semanticscholar.org/7b59/3d8949b1cafb1b97a945d50ae6eb839306c0.pdf
   Google Scholar
• Louwerse, D. J., & Smilde, A. K. (2000). Multivariate statistical process control of batch processes based on three-way models. Chemical engineering science, 55(7), 1225–1235. https://doi.org/https://doi.org/10.1016/S0009-2509(99)00408-X
   Web of Science ®Google Scholar
• Lu, H., Plataniotis, K. N., & Venetsanopoulos, A. N. (2008). MPCA: Multilinear principal component analysis of tensor objects. IEEE Transactions on Neural Networks, 19(1), 18–39. https://doi.org/https://doi.org/10.1109/TNN.2007.901277
   PubMed 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. https://doi.org/https://doi.org/10.1111/j.1751-5823.1997.tb00311.x
   Web of Science ®Google Scholar
• Martin, E. B., Morris, A. J., & Kiparissides, C. (1999). Manufacturing performance enhancement through multivariate statistical process control. Annual reviews in control, 23(1), 35–44. https://doi.org/https://doi.org/10.1016/S1367-5788(99)00005-X
   Google Scholar
• Martin, E. B., Morris, A. J., Papazoglou, M. C., & Kiparissides, C. (1996). Batch process monitoring for consistent production. Computers & chemical engineering, 20(96), S599–S604. https://doi.org/https://doi.org/10.1016/0098-1354(96)00109-3
   Google Scholar
• Meng, X., Morris, A. J., & Martin, E. B. (2003). On-line monitoring of batch processes using a PARAFAC representation. Journal of chemometrics, 17(1), 65–81. https://doi.org/https://doi.org/10.1002/cem.776
   Web of Science ®Google Scholar
• Neogi, D., & Schlags, C. E. (1998). Multivariate statistical analysis of an emulsion batch process. Industrial & engineering chemistry research, 37(10), 3971–3979. https://doi.org/https://doi.org/10.1021/ie980243o
   Web of Science ®Google Scholar
• Niang, N., Fogliatto, F. S., & Saporta, G. (2009). Batch process monitoring by three-way data analysis approach. Applied Stochastic Models and Data Analysis, 294–298. https://www.researchgate.net/publication/228424412_Batch_Process_Monitoring_by_Three-way_Data_Analysis_Approach
   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. https://cedric.cnam.fr/fichiers/art_2896.pdf
   Google Scholar
• Nomikos, P., & MacGregor, J. F. (1994). Monitoring batch processes using multiway principal component analysis. AIChE Journal, 40(8), 1361–1375. https://doi.org/https://doi.org/10.1002/aic.690400809
   Web of Science ®Google Scholar
• Nomikos, P., & MacGregor, J. F. (1995a). Multi-way partial least squares in monitoring batch processes. Chemometrics and Intelligent Laboratory Systems, 30(1), 97–108. https://doi.org/https://doi.org/10.1016/0169-7439(95)00043-7
   Web of Science ®Google Scholar
• Nomikos, P., & MacGregor, J. F. (1995b). Multivariate SPC charts for monitoring batch processes. Technometrics, 37(1) 41–59. https://doi.org/https://doi.org/10.1080/00401706.1995.10485888
   Web of Science ®Google Scholar
• Parra, L. C. (2018). Multi-set canonical correlation analysis simply explained. Nips. http://arxiv.org/abs/1802.03759
   Google Scholar
• Ramos-Barberán, M., Hinojosa-Ramos, M. V., Ascencio-Moreno, J., Vera, F., Ruiz-Barzola, O., & Galindo-Villardón, M. P. (2018). Batch process control and monitoring: A dual STATIS and parallel coordinates (DS-PC) approach. Production and Manufacturing Research, 6(1), 470–493. https://doi.org/https://doi.org/10.1080/21693277.2018.1547228
   Web of Science ®Google Scholar
• Rosipal, R., & Krämer, N. (2005). Overview and Recent Advances in Partial Least Squares. In: Saunders C., Grobelnik M., Gunn S., Shawe-Taylor J. (eds). Subspace, Latent Structure and Feature Selection. SLSFS 2005. Lecture Notes in Computer Science, vol 3940, (pp. 34–51). Springer, Berlin, Heidelberg. https://doi.org/https://doi.org/10.1007/11752790_2
   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). Physica-Verlag HD.
   Google Scholar
• Smilde, A., Bro, R., & Geladi, P. (2004). Multi-way analysis. Applications in the chemical sciences (L. John Wiley & Sons ed.). https://doi.org/https://doi.org/10.1002/0470012110
   Google Scholar
• Tates, A. A., Louwerse, D. J., Smilde, A. K., Koot, G. L. M., & Berndt, H. (1999). Monitoring a PVC batch process with multivariate statistical process control charts. Industrial & engineering chemistry research, 38(12), 4769–4776. https://doi.org/https://doi.org/10.1021/ie9901067
   Web of Science ®Google Scholar
• Tucker, L. R. (1966). Some mathematical notes on three-mode factor analysis. Psychometrika, 31(3), 279–311. https://doi.org/https://doi.org/10.1007/BF02289464
   PubMed Web of Science ®Google Scholar
• Ündey, C., Ertunç, S., & Çinar, A. (2003). Online batch/fed-batch process performance monitoring, quality prediction, and variable-contribution analysis for diagnosis. Industrial & engineering chemistry research, 42(20), 4645–4658. https://doi.org/https://doi.org/10.1021/ie0208218
   Web of Science ®Google Scholar
• Wang, Y., Jiang, Q., Li, B., & Cui, L. (2017). Joint-individual monitoring of parallel-running batch processes based on MCCA. IEEE Access, 6, 13005–13014. https://doi.org/https://doi.org/10.1109/ACCESS.2017.2784097
   Web of Science ®Google Scholar
• Wegman, E. (1990). Hyperdimensional data analysis using parallel coordinates. Journal of the American Statistical Association, 85(411), 664–675. https://doi.org/https://doi.org/10.1080/01621459.1990.10474926
   Web of Science ®Google Scholar
• Wise, B. M., Gallagher, N. B., & Martin, E. B. (2001). Application of PARAFAC2 to fault detection and diagnosis in semiconductor etch. Journal of chemometrics, 15(4), 285–298. https://doi.org/https://doi.org/10.1002/cem.689
   Web of Science ®Google Scholar
• Wiskott, L. (1999). Learning invariance manifolds. Neurocomputing, 26–27, 925–932. https://doi.org/https://doi.org/10.1016/S0925-2312(99)00011-9
   Web of Science ®Google Scholar
• Wiskott, L., & Sejnowski, T. J. (2002). Slow feature analysis: Unsupervised learning of invariances. Neural computation, 14(4), 715–770. https://doi.org/https://doi.org/10.1162/089976602317318938
   PubMed Web of Science ®Google Scholar
• Wold, S., Geladi, P., Esbensen, K., & Öhman, J. (1987). Multi-way principal components and PLS-Analysis. Journal of chemometrics, 1(1), 41–56. https://doi.org/https://doi.org/10.1002/cem.1180010107
   Google Scholar
• Woodall, W. H., & Montgomery, D. C. (1999). Research issues and ideas in statistical process control. Journal of Quality Technology, 31(4), 376–386. https://doi.org/https://doi.org/10.1080/00224065.1999.11979944
   Web of Science ®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. https://doi.org/https://doi.org/10.1016/j.chemolab.2004.02.002
   Web of Science ®Google Scholar
• Yu, J., Chen, J., & Rashid, M. M. (2013). Multiway independent component analysis mixture model and mutual information based fault detection and diagnosis approach of multiphase batch processes. AIChE Journal, 59(8), 2761–2779. https://doi.org/https://doi.org/10.1002/aic.14051
   Web of Science ®Google Scholar
• Yuan, B., & Wang, X. Z. (2001). Multilevel PCA and inductive learning for knowledge extraction from operational data of batch processes. Chemical engineering communications, 185(October2014), 201–221. https://doi.org/https://doi.org/10.1080/00986440108912863
   Google Scholar
• Zafeiriou, L., Nicolaou, M. A., Zafeiriou, S., Nikitidis, S., & Pantic, M. (2013). Learning slow features for behaviour analysis. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia. 2840–2847. https://doi.org/https://doi.org/10.1109/ICCV.2013.353
   Google Scholar
• Zhang, H., Tian, X., & Deng, X. (2017). Batch process monitoring based on multiway global preserving kernel slow feature analysis. IEEE Access, 5, 2696–2710. https://doi.org/https://doi.org/10.1109/ACCESS.2017.2672780
   Web of Science ®Google Scholar