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Multivariate quality control of batch processes using STATIS

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An Erratum to this article was published on 26 May 2016

Abstract

Industrial batch processes are commonly used in the production of a variety of items. Data emerging from such processes present a peculiar structure, and a number of customized multivariate control charts (CCs) have been proposed for their monitoring. In this paper, we propose CCs based on STATIS method, an exploratory technique for measuring similarities between data matrices. Data are arranged in such a way that the monitoring along time is prioritized. The methodology easily allows a nonparametric online monitoring of complex batch processes in time, in situations where a large number of variables are present. Besides its presentation, the new approach is illustrated using simulated data.

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References

  1. Jackson JE, Mudholkar GS (1979) Control procedures for residuals associated with principal component analysis. Technometrics 21:341–349

    Article  MATH  Google Scholar 

  2. Nomikos P, Macgregor JF (1995) Multivariate SPC charts for monitoring batch processes. Technometrics 37:41–59

    Article  MATH  Google Scholar 

  3. Kourti T, Nomikos P, Macgregor JF (1995) Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS. J Process Control 5(3):277–284

    Article  Google Scholar 

  4. Kourti T, Macgregor JF (1996) Multivariate SPC methods for process and product monitoring. J Qual Technol 28(3):409–428

    Google Scholar 

  5. Macgregor JF (1997) Using on-line process data to improve quality: Challenges for statisticians. Int Stat Rev 65:309–323

    Article  MATH  Google Scholar 

  6. Kourti T (2003) Multivariate dynamic data modeling for analysis and statistical process control of batch process, start-ups and grade transitions. J Chemom 17:93–109

    Article  Google Scholar 

  7. Jolliffe IT (2004) Principal component analysis, 2nd edn. Springer, New York

    MATH  Google Scholar 

  8. Majid NAA, Taylor MP, Chen JJJ, Stam MA, Mulder A, Young BR (2011) Aluminium process fault detection by multiway principal components. Control Eng Pract 19(3):367–379

    Article  Google Scholar 

  9. Chen J-H, Wan W-Y (2010) Performance monitoring of MPCA-based control for multivariate batch control. J Taiwan Inst Chem Eng 41(3):465–474

    Article  Google Scholar 

  10. Chang Y-Q, Lu Y-S, Wang F-L, Wang S, Feng S-M (2012) Sub-stage PCA modelling and monitoring method for uneleven-length batch process. Can J Chem Eng 90(1):144–152

    Article  Google Scholar 

  11. Chen J, Jiang Y-C (2011) Hidden semi-Markov probability models for monitoring two-dimensional batch operation. Ind Eng Chem Res 50:3345–3355

    Article  Google Scholar 

  12. Badamoradi H, Van den Berg F, Rinnan A (2013) Comparison of bootstrap and asynptotic confidence limits for control charts in batch MSPC strategies. Chemom Intell Lab Syst 127:102–111

    Article  Google Scholar 

  13. Ramarker H-J, Van Sprang ENM, Westerhuis JA, Smilde AK (2005) Fault detection properties of global, local and time evolving models for batch process monitoring. J Process Control 15:799–805

    Article  Google Scholar 

  14. Chiang LH, Leardi R, Pell RJ, Seasholtz MB (2006) Industrial experiences with multivariate statistical analysis of batch process data. Chemom Intell Lab Syst 81:109–119

    Article  Google Scholar 

  15. Ferreira AP, Lopes JA, Menezes JC (2007) Study of the application of multiway multivariate techniques to model data from an industrial fermentation process. Anal Chim Acta 595:120–127

    Article  Google Scholar 

  16. Camacho J, Picó J, Ferrer A (2009) The best approaches in the on-line monitoring of batch processes based on CA: does the modeling structure matter? Anal Chim Acta 642:59–68

    Article  Google Scholar 

  17. Peng J, Liu H, Hu Y, Xi J, Chen H (2014) ASCS on-line fault detection and isolation based on an improved MPCA. Chin J Mech Eng 27(5):1047–1056

    Article  Google Scholar 

  18. Ge Z, Song Z, Gao F (2013) Review of recent research on data-based process monitoring. Ind Eng Chem Res 52:3543–3562

    Article  Google Scholar 

  19. Li Y, Zhang X (2015) Variable moving windows based non-gaussian dissimilarity analysis technique for batch processes fault detection and diagnosis. Can J Chem Eng 93:689–707

    Article  Google Scholar 

  20. Huang H-P, Wu M-C (2003) Monitoring and fault detection for dynamics systems using dynamic PCA on filtered data. Process Syst Eng 846–851

  21. Jia M, Chu F, Wang F, Wang W (2010) On-line batch process monitoring using batch dynamic kernel principal component analysis. Chemom Intell Lab Syst 101:110–122

    Article  Google Scholar 

  22. Lee J-M, Yoo CY, Lee I-B (2003) On-line process monitoring using different unfolding method and independent component analysis. J Chem Eng Jpn 36(11):1384–1396

    Article  Google Scholar 

  23. Escoufier Y (1987) Three-mode data analysis: the STATIS method. In: ECAS, Fichet B, Lauro NC (eds) Methods for multidimencional data analysis, 259–272

  24. Lavit C, Escoufier Y, Sabatier R, Traissac P (1994) The ACT (STATIS method). Comput Stat Data Anal 19:97–119

    Article  MathSciNet  MATH  Google Scholar 

  25. Stanimirova I, Walczak B, Massart DL, Simeonov V, Saby CA, Di Crescenzo E (2004) STATIS, a three-way method for data analysis. Application to environmental data. Chemom Intell Lab Syst 73(2):219–233

    Article  Google Scholar 

  26. Fournier M, Motelay-Massei A, Massei N, Aubert M, Bakalowicz M, Dupont JP (2009) Investigation of transport processes inside karst aquifer by means of STATIS. Ground Water 47(3):391–400

    Article  Google Scholar 

  27. Scepi G (2002) Parametric and non parametric multivariate quality control charts. In: Lauro C et al (eds.) Multivariate Total Quality Control, Physica-Verlang, 163–189

  28. Zani S, Riani M, Corbellini A (1998) Robust bivariate boxplots and multiple outlier detection. Comput Stat Data Anal 28:257–270

    Article  MATH  Google Scholar 

  29. Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning—Data mining, inference, and prediction. Springer Science, New York

    MATH  Google Scholar 

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Authors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil

    Danilo Marcondes Filho

  2. Vale do Rio dos Sinos, Porto Alegre, Rio Grande do Sul, Brazil

    Luiz Paulo Luna de Oliveira

Authors
  1. Danilo Marcondes Filho
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  2. Luiz Paulo Luna de Oliveira
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Correspondence to Danilo Marcondes Filho.

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Filho, D.M., de Oliveira, L.P.L. Multivariate quality control of batch processes using STATIS. Int J Adv Manuf Technol 82, 867–875 (2016). https://doi.org/10.1007/s00170-015-7428-0

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  • DOI: https://doi.org/10.1007/s00170-015-7428-0

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