ART-Type Artificial Neural Networks Applications for Classification of Operational States in Wind Turbines

  • Tomasz Barszcz
  • Andrzej Bielecki
  • Mateusz Wójcik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6114)


In recent years wind energy is the fastest growing branch of the power generation industry. The largest cost for the wind turbine is its maintenance. A common technique to decrease this cost is a remote monitoring based on vibration analysis. Growing number of monitored turbines requires an automated way of support for diagnostic experts. As full fault detection and identification is still a very challenging task, it is necessary to prepare an ”early warning” tool, which would focus the attention on cases which are potentially dangerous. There were several attempts to develop such tools, in most cases based on various classification methods (predominantly neural networks). Due to very common lack of sufficient data to perform training of a method, the important problem is the need for creation of new states when there are data different from all known states.

As the ART neural networks are capable to perform efficient classification and to recognize new states when necessary, they seems to be a proper tool for classification of operational states in wind turbines. The verification of ART and fuzzy-ART networks efficiency in this task is the topic of this paper.


Wind Turbine Wind Energy Input Pattern Human Expert Remote Monitoring 
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 2010

Authors and Affiliations

  • Tomasz Barszcz
    • 1
  • Andrzej Bielecki
    • 2
  • Mateusz Wójcik
    • 3
  1. 1.Chair of Robotics and MechatronicsAGH University of Science and TechnologyKrakówPoland
  2. 2.Institute of Computer ScienceJagiellonian UniversityKrakówPoland
  3. 3.Department of Computer Design and GraphicsJagiellonian UniversityKrakówPoland

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