Abstract
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.
The paper was supported by the Polish Ministry of Science and Higher Education under Grant No. N504 147838.
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Barszcz, T., Bielecki, A., Wójcik, M. (2010). ART-Type Artificial Neural Networks Applications for Classification of Operational States in Wind Turbines. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artifical Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13232-2_2
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DOI: https://doi.org/10.1007/978-3-642-13232-2_2
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