Neural Computing and Applications

, Volume 23, Issue 5, pp 1407–1419 | Cite as

Visualization maps based on SOM to analyze MIMO systems

  • J. J. Fuertes
  • M. Domínguez
  • I. Díaz
  • M. A. Prada
  • A. Morán
  • S. Alonso
Original Article

Abstract

Knowledge extraction from large amounts of data is an effective approach for analysis and monitoring of industrial processes. The self-organizing map (SOM) is a useful method for this purpose, because it is able to discover low-dimensional structures on high-dimensional spaces and produce a mapping on an ordered low-dimensional space that can be visualized and preserves the most important relationships. With the aim to extract knowledge about the dynamics of industrial processes, we define 2D SOM maps that represent dynamic features which are useful for usual tasks in control engineering such as the analysis of the time response, the coupling among variables, or the difficulties in control of MIMO (multiple-input and multiple-output) systems. Those new maps make it possible to discover, increase or confirm knowledge about the system, spanned through the entire operation range. A well-known quadruple-tank MIMO system was used to test the usefulness of these maps. First, we perform an analysis of the theoretical dynamic behaviors obtained from the physical equations of the system. After that, we carry out an analysis of experimental data from an industrial pilot plant.

Keywords

Self-organizing map Industrial processes Information visualization Dynamics MIMO system 

Notes

Acknowledgments

This work was supported in part by the Spanish Ministerio de Ciencia e Innovación (MICINN) and the European FEDER funds under grants DPI2009-13398-C02-01 and DPI2009-13398-C02-02. A. Morán was supported by a grant from the Consejería de Educación de la Junta de Castilla y León and the European Social Fund.

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Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • J. J. Fuertes
    • 1
  • M. Domínguez
    • 1
  • I. Díaz
    • 2
  • M. A. Prada
    • 1
  • A. Morán
    • 1
  • S. Alonso
    • 1
  1. 1.Instituto de Automática y FabricaciónUniversidad de León, Grupo de Investigación SUPPRESSLeónSpain
  2. 2.Área de Ingeniería de Sistemas y AutomáticaUniversidad de OviedoGijónSpain

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