Application of SOM-Based Visualization Maps for Time-Response Analysis of Industrial Processes

  • Miguel A. Prada
  • Manuel Domínguez
  • Ignacio Díaz
  • Juan J. Fuertes
  • Perfecto Reguera
  • Antonio Morán
  • Serafín Alonso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6353)

Abstract

Self-organizing maps have been extensively used for visualization of industrial processes. Nevertheless, most of these approaches lack insight about the dynamic behavior. Recently, an approach to define visualizable maps of dynamics from data has been proposed. We propose the application of this approach to single-input single-output processes by defining several maps related to relevant features in the time-response analysis. This features are commonly used in control engineering. We show that these maps are intuitive and consistent tools for knowledge discovery and validation. They also provide a general overview of the process behavior and can be used along with other previously defined maps for process analysis and monitoring.

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References

  1. 1.
    Venkatasubramanian, V., Rengaswamy, R., Kavuri, S., Yin, K.: A review of process fault detection and diagnosis part III: Process history based methods. Computers and Chemical Engineering 27(3), 327–346 (2003)CrossRefGoogle Scholar
  2. 2.
    Keim, D.A.: Information visualization and visual data mining. IEEE Trans. Vis. Comput. Graph. 8(1), 1–8 (2002)CrossRefGoogle Scholar
  3. 3.
    Alhoniemi, E., Hollmén, J., Simula, O., Vesanto, J.: Process monitoring and modeling using the self-organizing map. Integrated Computer-Aided Engineering 6(1), 3–14 (1999)Google Scholar
  4. 4.
    Lee, J.A., Verleysen, M.: Nonlinear Dimensionality Reduction. In: Information Science and Statistics. Springer, Heidelberg (2007)Google Scholar
  5. 5.
    Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, New York (2001)MATHGoogle Scholar
  6. 6.
    Kohonen, T., Oja, E., Simula, O., Visa, A., Kangas, J.: Engineering applications of the self-organizing map. Proceedings of the IEEE 84(10), 1358–1384 (1996)CrossRefGoogle Scholar
  7. 7.
    Jämsä-Jounela, S.L., Vermasvuori, M., Endén, P., Haavisto, S.: A process monitoring system based on the Kohonen self-organizing maps. Control Eng. Practice 11(1), 83–92 (2003)CrossRefGoogle Scholar
  8. 8.
    Abonyi, J., Nemeth, S., Csaba, V., Vesanto, P.A.J.: Process analysis and product quality estimation by self-organizing maps with an application to polyethylene production. Computers in Industry 52(3), 221–234 (2003)CrossRefGoogle Scholar
  9. 9.
    Simula, O., Kangas, J.: Process monitoring and visualisation using Self-Organizing Maps. In: Neural Networks for Chemical Engineers, pp. 377–390. Elsevier Science B.V, Amsterdam (1995)Google Scholar
  10. 10.
    Fuertes, J.J., Prada, M.A., Domínguez, M., Reguera, P., Díaz, I., Cuadrado, A.A.: Modeling of Dynamics using Process State Projection on the Self Organizing Map. In: de Sá, J.M., Duch, W., Alexandre, L.A., Mandic, D.P. (eds.) ICANN 2007, Part I. LNCS, vol. 4668, pp. 589–598. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Principe, J., Wang, L., Motter, M.: Local dynamic modeling with self-organizing maps and applications to nonlinear system identification and control. Proc. IEEE 86(11), 2240–2258 (1998)CrossRefGoogle Scholar
  12. 12.
    Díaz, I., Domínguez, M., Cuadrado, A.A., Fuertes, J.J.: A new approach to exploratory analysis of system dynamics using SOM. Applications to industrial processes. Expert Systems with Applications 34(4), 2953–2965 (2008)CrossRefGoogle Scholar
  13. 13.
    Cho, J., Principe, J.C., Erdogmus, D., Motter, M.A.: Modeling and inverse controller design for an unmaned aerial vehicle based on the self-organizing map. IEEE Trans. Neural Networks 17(2), 445–460 (2006)CrossRefGoogle Scholar
  14. 14.
    Basseville, M.: Distance measures for signal processing and pattern recognition. Signal Processing 18, 349–369 (1989)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Keogh, E., Kasetty, S.: On the need for time series data mining benchmarks: A survey and empirical demonstration. In: Proc. of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 102–111 (2002)Google Scholar
  16. 16.
    Ogata, K.: Modern Control Engineering, 4th edn. Prentice Hall, Englewood Cliffs (2001)Google Scholar
  17. 17.
    Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Transactions on Neural Networks 11(3), 586–600 (2000)CrossRefGoogle Scholar
  18. 18.
    Xu, R., Wunsch II, D.C.: Clustering. IEEE Press Series on Computational Intelligence. Wiley-IEEE Press (October 2008)Google Scholar
  19. 19.
    Ljung, L.: System Identification: Theory for the User, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Miguel A. Prada
    • 1
  • Manuel Domínguez
    • 2
  • Ignacio Díaz
    • 3
  • Juan J. Fuertes
    • 2
  • Perfecto Reguera
    • 2
  • Antonio Morán
    • 2
  • Serafín Alonso
    • 2
  1. 1.Dept. of Information and Computer ScienceAalto University School of Science and TechnologyAaltoFinland
  2. 2.Dept. de Ing. Elétrica y de Sistemas y AutomáticaUniversidad de León, Esc. de Ing. Industrial e InformáticaLeónSpain
  3. 3.Dept. de Ing. Elétrica, Electrónica, de Computadores y SistemasUniversidad de OviedoGijónSpain

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