Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Intelligent Decision Making Techniques in Quality Management: A Literature Review Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

9. Multivariate Statistical and Computational Intelligence Techniques for Quality Monitoring of Production Systems

verfasst von : Tibor Kulcsár, Barbara Farsang, Sándor Németh, János Abonyi

Erschienen in: Intelligent Decision Making in Quality Management

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The ISO 9001:2008 quality management standard states that organizations shall plan and implement monitoring, measurement, analysis and improvement processes to demonstrate conformity to product requirements. According to the standard, detailed analysis of data is required for this purpose. The analysis of data should also provide information related to characteristics and trends of processes and products, including opportunities for preventive action. The preliminary aim of this chapter is to show how intelligent techniques can be used to design data–driven tools that are able to support the organization to continuously improve the effectiveness of their production according to the Plan—Do—Check—Act (PDCA) methodology. The chapter focuses on the application of data mining and multivariate statistical tools for process monitoring and quality control. Classical multivariate tools such as PLS and PCA are presented along with their nonlinear variants. Special attention is given to software sensors used to estimate product quality. Practical application examples taken from chemical and oil and gas industries illustrate the applicability of the discussed techniques.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel The Role of Computational Intelligence in Experimental Design: A Literature Review
Nächstes Kapitel Failure Mode and Effects Analysis Under Uncertainty: A Literature Review and Tutorial
Literatur
Abbas, O., et al.: PLS regression on spectroscopic data for the prediction of crude oil quality: API gravity and aliphatic/aromatic ratio. Fuel 98, 5–14 (2012)CrossRef
Abonyi, J., Feil, B.: Computational intelligence in data mining. Informatica 29, 3–12 (2005)
Abonyi, J., Babuska, R., Szeifert, F.: Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models. IEEE Trans. Syst. Man Cybern. B Cybern. 32(5), 612–621 (2002)CrossRef
Alander, J.T., et al.: Process error detection using self-organizing feature maps, Artif. Neural Netw. 2, 1229–1232 (1991)
Alhoniemi, E., et al.: Process monitoring and modeling using the self-organizing map. Integr. Comput. Aided Eng. 6, 3–14 (1999)
Astudillo, C.A., Oommen, B.J.: Self-organizing maps whose topologies can be learned with adaptive binary search trees using conditional rotations. Pattern Recogn. 47, 96–113 (2014)CrossRef
Bao, X., Dai, L.: Partial least squares with outlier detection in spectral analysis: a tool to predict gasoline properties. Fuel 88(7), 1216–1222 (2009)CrossRef
Borosy, A.P.: Quantitative composition-property modelling of rubber mixtures by utilizing artificial neural networks. Chemom. Intell. Lab. Syst. 47, 227–238 (1998)
Chen, G., McAvoy, T.J., Piovoso, M.J.: A multivariate statistical controller for on-line quality improvement. J. Process Control 8(2), 139–149 (1998)CrossRef
Chiang, L.H., Russel, E.L., Braatz, R.D.: Fault Detection and Diagnosis in Industrial Systems. Springer, London (2001)CrossRefMATH
Ergon, R.: Informative PLS score-loading plots for process understanding and monitoring. J. Process Control 14, 889–897 (2004)MathSciNetCrossRef
Fujiwara, K., Sawada, H., Kano, M.: Input variable selection for PLS modeling using nearest correlation spectral clustering. Chemometr. Intell. Lab. Syst. 118, 109–119 (2012)CrossRef
Fustes, D., et al.: SOM ensemble for unsupervised outlier analysis. Application to outlier identification in the Gaia astronomical survey. Expert Syst. Appl. 40(5), 1530–1541 (2013)CrossRef
Garcia-Ruiz, R., et al.: Suitability of enzyme activities for the monitoring of soil quality improvement in organic agricultural systems. Soil Biol. Biochem. 40(9), 2137–2145 (2008)CrossRef
Ge, Z.: Two-level PLS model for quality prediction of multiphase batch processes. Chemometr. Intell. Lab. Syst. 130, 29–36 (2014)CrossRef
Ghosh, S., Roy, M., Ghosh, A.: Semi-supervised change detection using modified self-organizing feature map neural network. Appl. Soft Comput. 15, 1–20 (2014)CrossRef
Godoy, J.L., Vega, J.R., Marchetti, J.L.: Relationships between PCA and PLS-regression. Chemom. Intell. Lab. Syst. 130, 182–191 (2014)
Harris, T., Kohonen, A.: S.O.M. based, machine health monitoring systems which enables diagnosis of faults not seen in the training set. Proc. Int. Conf. Neural Netw. (IJCNN’93) Nagoya, Japan 1, 947–950 (1993)
Hu, W., Storer, R., Georgakis, C.: Disturbance detection and isolation by dynamic principal component analysis. Chemometr. Intell. Lab. Syst. 30(1), 179–196 (1995)CrossRef
Ivanova, I., Kubat, M.: Initialization of neural networks by means of decision trees. Knowl. Based Syst. 8, 333–344 (1995)CrossRef
Jang, J.-S.R., Sun, C.T.: Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Trans. Neural Netw. 4, 156–159 (1993)
Janné, K., et al.: Hierarchical principal component analysis (PCA) and projection to latent structure (PLS) technique on spectroscopic data as a data pretreatment for calibration. J. Chemom. 15(4), 203–213 (2001). doi:10.​1002/​cem.​677 CrossRef
Jeackle, C., MacGregor, J.: Product design through multivariate statistical analysis of process data. Am. Inst. Chem. Eng. J. 44(5), 1105–1118 (1998)CrossRef
Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer Series in Statistics (2008)
Juntunen, P., et al.: Cluster analysis by self-organizing maps: an application to the modelling of water quality in a treatment process. Appl. Soft Comput. 13(7), 3191–3196 (2013)CrossRef
Kalteh, A.M., Hjorth, P., Berndtsson, R.: Review of the self-organizing map (SOM) approach in water resources: analysis, modelling and application. Environ. Model Softw. 23(7), 835–845 (2008)CrossRef
Kano, M., Nakagawa, Y.: Data-based process monitoring, process control, and quality improvement: recent developments and applications in steel industry. Comput. Chem. Eng. 32(1–2), 12–24 (2008)CrossRef
Karlsson, A.: The use of principal component analysis (PCA) for evaluating results from pig meat quality measurements. Meat Sci. 31(4), 423–433 (1992)CrossRef
Kassalin, M., Kangas, J., Simula, O.: Process state monitoring using self-organizing maps. Artif. Neural Netw. 2, 1531–1534 (1992)CrossRef
Kenesei, T., Abonyi, J.: Hinging hyperplane based regression tree identified by fuzzy clustering and its application. Appl. Soft Comput. J. 13(2), 782–792 (2013)CrossRef
Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)CrossRef
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer Series in Information Sciences (2001)
Kresta, J.V., Macgregor, F.F., Marlin, T.E.: Multivariate statistical monitoring of process operating performance. Can. J. Chem. Eng. 69(1), 35–47 (1992). doi:10.​1002/​cjce.​5450690105
Lakshminarayanan, S., et al.: New product design via analysis of historical databases. Comput. Chem. Eng. 24, 671–676 (2000)CrossRef
Lue, H.-H.: Sliced inverse regression for multivariate response regression. J. Stat. Plan. Inference 139, 2656–2664 (2009)
MacGregor, J.F., Kourti, T.: Statistical process control of multivariate processes. Control Eng. Pract. 3(3), 403–414 (1995)CrossRef
Martin, E.B., et al.: Batch process monitoring for consistent production. Comput. Chem. Eng. 20, S599–S605 (1996)CrossRef
Mele, P.M., Crowley, D.E.: Application of self-organizing maps for assessing soil biological quality. Agric. Ecosyst. Environ. 126(3–4), 139–152 (2008)CrossRef
Montgomery D.C.: Introduction to Statistical Quality Control, John Wiley, New York (2009)
Moteki, Y., Arai, Y.: Operation planning and quality design of a polymer process. In: IFAC DYCORD, pp. 159–165 (1986)
Munoz, A., Muruzábal, J.: Self-organizing maps for outlier detection. Neurocomputing 18, 33–60 (1998)CrossRef
Murray-Smith, R., Johansen, T.A.: Multiple Model Approaches to Nonlinear Modeling and Control. Taylor & Francis, London (1997)
Nagy, G.: The polyethylene, Magyar Kémikusok Lapja (MKL). Hungary 52(5), 233–242 (1997)
Nelson, P.R.C., MacGregor, J.F., Taylor, P.A.: The impact of missing measurements on PCA and PLS prediction and monitoring applications. Chemometr. Intell. Lab. Syst. 80(1), 1–12 (2006)CrossRef
Nicolai, B.M., et al.: Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol. Technol. 46(2), 99–118 (2007)CrossRef
Ochocka, R.J., Wesolowski, M., Lamparczyk, H.: Thermoanalysis supported by principal component analysis (PCA) in quality assessment of essential oil samples. Thermochim. Acta 210, 151–162 (1992)CrossRef
Olawoyin, R., et al.: Application of artificial neural network (ANN)–self-organizing map (SOM) for the categorization of water, soil and sediment quality in petrochemical regions. Expert Syst. Appl. 40(9), 3634–3648 (2013)CrossRef
Pereira, A.F.C., et al.: NIR spectrometric determination of quality parameters in vegetable oils using iPLS and variable selection. Food Res. Int. 41(4), 341–348 (2008)CrossRef
Poggy, G., Cozzolino, D., Verdoliva, L.: Self-organizing maps for the design of multiple description vector quantizers. Neurocomputing 122, 298–309 (2013)CrossRef
Principe, J.C., Wang, L., Motter, M.A.: Local dynamic modeling with self-organizing maps and applications to nonlinear system identification and control. Proc. IEEE 86(11), 2241–2258 (1998)CrossRef
Sánchez-Martos, F., Jiménez-Espinosa, R., Pulido-Bosch, A.: Mapping groundwater quality variables using PCA and geostatistics: a case study of Bajo Andarax, southeastern Spain. Hydrol. Sci. J.-des Sciences Hydrologiques 46(2), 227–242 (2001)CrossRef
Sethi, L.K.: Entropy nets: from decision trees to neural networks. Proc. IEEE 78, 1605–1613 (1990)CrossRef
Simula, O., et al.: Analysis and modeling of complex systems using the self-organizing map. In Neuro-Fuzzy Techniques for Intelligent Information Systems, pp. 3–22. Springer, New York (1999)
Steenkamp, J.B.E.M., van Trijp, H.C.M.: Quality guidance: a consumer-based approach to food quality improvement using partial least squares. Eur. Rev. Agric. Econ. 23(2), 195–215 (1996). doi:10.​1093/​erae/​23.​2.​195 CrossRef
Tryba, V., Goser, K.: Self-organizing feature maps for process control in chemistry. Artif. Neural Netw. 847–852 (1991)
Valova, I., et al.: Initialization Issues in Self-organizing Maps. Procedia Comput. Sci. 20, 52–57 (2013)CrossRef
Vinzi, V., et al.: Handbook of Partial Least Squares. In: Springer Handbooks of Computational Statistics (2010)
Wang, D., Srinivasan, R.: Eliminating the effect of multivariate outliers in pls-based models for inferring process quality. Comput. Aided Chem. Eng. 26, 755–760 (2009)CrossRef
Wang X.Z.: Data Mining and Knowledge Discovery for Process Monitoring and Control. Springer, New York (1999)
Yamashita, Y.: Supervised learning for the analysis of the process operational data. Comput. Chem. Eng. 24, 471–474 (2000)CrossRef
Zadeh, L.A.: Soft computing and fuzzy logic. Software, IEEE 11(6), 48–56 (1994)
Zhang, J., Martin, E.B., Morris, A.J.: Process monitoring using non-linear statistical techniques. Chem. Eng. J. 67, 181–189 (1997)CrossRef
Metadaten
Titel
Multivariate Statistical and Computational Intelligence Techniques for Quality Monitoring of Production Systems
verfasst von
Tibor Kulcsár
Barbara Farsang
Sándor Németh
János Abonyi
Copyright-Jahr
2016
Buch
Intelligent Decision Making in Quality Management

Print ISBN: 978-3-319-24497-6

Electronic ISBN: 978-3-319-24499-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-24499-0_9