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http://dx.doi.org/10.5050/KSNVE.2013.23.9.814

Fault Diagnosis of a Rotating Blade using HMM/ANN Hybrid Model  

Kim, Jong Su (Mechanical Engineering Department, Hanyang University)
Yoo, Hong Hee (Mechanical Engineering Department, Hanyang University)
Publication Information
Transactions of the Korean Society for Noise and Vibration Engineering / v.23, no.9, 2013 , pp. 814-822 More about this Journal
Abstract
For the fault diagnosis of a mechanical system, pattern recognition methods have being used frequently in recent research. Hidden Markov model(HMM) and artificial neural network(ANN) are typical examples of pattern recognition methods employed for the fault diagnosis of a mechanical system. In this paper, a hybrid method that combines HMM and ANN for the fault diagnosis of a mechanical system is introduced. A rotating blade which is used for a wind turbine is employed for the fault diagnosis. Using the HMM/ANN hybrid model along with the numerical model of the rotating blade, the location and depth of a crack as well as its presence are identified. Also the effect of signal to noise ratio, crack location and crack size on the success rate of the identification is investigated.
Keywords
Hidden Markov Model(HMM); Artificial Neural Network(ANN); Fault Diagnosis; Feature Vector; Vector Quantization;
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Times Cited By KSCI : 1  (Citation Analysis)
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