A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Soliciting customer requirements for product redesign based on picture sorts and ART2 neural network
Expert Systems with Applications: An International Journal
Performance evaluation for classification methods: A comparative simulation study
Expert Systems with Applications: An International Journal
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
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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.