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http://dx.doi.org/10.3795/KSME-A.2014.38.2.205

Application of Excitation Moment for Enhancing Fault Diagnosis Probability of Rotating Blade  

Kim, Jong Su (Dept. of Mechanical Engineering, Hanyang Univ.)
Choi, Chan Kyu (Dept. of Mechanical Engineering, Hanyang Univ.)
Yoo, Hong Hee (Dept. of Mechanical Engineering, Hanyang Univ.)
Publication Information
Transactions of the Korean Society of Mechanical Engineers A / v.38, no.2, 2014 , pp. 205-210 More about this Journal
Abstract
Recently, pattern recognition methods have been widely used by researchers for fault diagnoses of mechanical systems. A pattern recognition method determines the soundness of a mechanical system by detecting variations in the system's vibration characteristics. Hidden Markov models (HMMs) and artificial neural networks (ANNs) have recently been used as pattern recognition methods in various fields. In this study, a HMM-ANN hybrid method for the fault diagnosis of a mechanical system is introduced, and a rotating wind turbine blade with a crack is selected for fault diagnosis. The existence, location, and depth of said crack are identified in this research. For improving the diagnostic accuracy of the method in spite of the presence of noise, a moment with a few specific frequencies is applied to the structure.
Keywords
Hidden Markov Model(HMM); Artificial Neural Network(ANN); Fault Diagnosis; Feature Vector; Vector Quantization;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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