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Process State and Progress Visualization Using Self-Organizing Map

  • Risto Hakala
  • Timo Similä
  • Miki Sirola
  • Jukka Parviainen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

The self-organizing map (SOM) [1] is used in data analysis for resolving and visualizing nonlinear relationships in complex data. This paper presents an application of the SOM for depicting state and progress of a real-time process. A self-organizing map is used as a visual regression model for estimating the state configuration and progress of an observation in process data. The proposed technique is used for examining full-scope nuclear power plant simulator data. One aim is to depict only the most relevant information of the process so that interpretating process behaviour would become easier for plant operators. In our experiments, the method was able to detect a leakage situation in an early stage and it was possible to observe how the system changed its state as time went on.

Keywords

Training Data Process Variable Component Plane Reactor Shutdown Boiling Water Reactor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Risto Hakala
    • 1
  • Timo Similä
    • 1
  • Miki Sirola
    • 1
  • Jukka Parviainen
    • 1
  1. 1.Laboratory of Computer and Information ScienceHelsinki University of Technology, HUTFinland

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