한국소음진동공학회논문집 (Transactions of the Korean Society for Noise and Vibration Engineering)

  • 제23권9호
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  • Pages.814-822
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  • 2013
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  • 1598-2785(pISSN)
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  • 2287-5476(eISSN)
한국소음진동공학회 (The Korean Society for Noise and Vibration Engineering)
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HMM/ANN복합 모델을 이용한 회전 블레이드의 결함 진단

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)
  • 투고 : 2013.06.17
  • 심사 : 2013.09.14
  • 발행 : 2013.09.20
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초록

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.

키워드

참고문헌

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피인용 문헌

  1. Application of Excitation Moment for Enhancing Fault Diagnosis Probability of Rotating Blade vol.38, pp.2, 2014, https://doi.org/10.3795/KSME-A.2014.38.2.205