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http://dx.doi.org/10.5391/JKIIS.2014.24.1.078

Development of intelligent fault diagnostic system for mechanical element of wind power generator  

Moon, Dea-Sun (School of Electronic & Information Engineering, Kunsan University)
Kim, Sung-Ho (Department of Control & Robotics Engineering Kunsan University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.24, no.1, 2014 , pp. 78-83 More about this Journal
Abstract
Recently, a rapid growth of wind power system as a leading renewable energy source has compelled a number of companies to develop intelligent monitoring and diagnostic system. Such systems can detect early mechanical faults, which prevents from costly repairs. Generally, fault diagnostic system for wind turbines is based on vibration and process signal analysis. In this work, different type of mechanical faults such as mass unbalance and shaft misalignment which can always happen in wind turbine system is considered. The proposed intelligent fault diagnostic algorithm utilizes artificial neural network and Wavelet transform. In order to verify the feasibility of the proposed algorithm, mechanical fault generation experimental system manufactured by Gaon corporation is utilized.
Keywords
Monitoring & diagnostic system; Mechanical fault; Mass unbalance; Shaft misalignment; Artificial neural network; Wavelet transform;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
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