대한기계학회논문집A (Transactions of the Korean Society of Mechanical Engineers A)

  • 제38권2호
  • /
  • Pages.205-210
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  • 2014
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  • 1226-4873(pISSN)
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  • 2288-5226(eISSN)
대한기계학회 (The Korean Society of Mechanical Engineers)
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회전 블레이드의 결함진단 확률제고를 위한 가진 모멘트 적용

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

  • 김종수 (한양대학교 기계공학부) ;
  • 최찬규 (한양대학교 기계공학부) ;
  • 유홍희 (한양대학교 기계공학부)
  • 투고 : 2013.11.26
  • 심사 : 2013.12.12
  • 발행 : 2014.02.01
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초록

기계시스템의 결함을 진단하기 위한 방법으로 패턴인식 기법이 널리 사용되고 있다. 진동신호의 변화를 감지하여 기계시스템의 건전성을 판단하는 방법이 패턴인식 기법이다. 대표적 패턴 인식기법으로 최근 은닉 마르코프 모델과 인공신경망이 여러 분야에서 사용되고 있다. 본 연구에서는 결함진단에 은닉 마르코프 모델과 인공신경망을 혼합한 방법이 제시되었으며 결함진단 대상 구조물로는 크랙을 가진 회전하는 풍력터빈 블레이드가 선정되었다. 본 연구에서는 크랙발생 여부뿐만 아니라 그 위치 및 크기도 동시에 진단하고자 하였다. 아울러서 본 연구에서는 일정 주파수들을 갖는 모멘트를 대상 구조물에 가함으로써 외부 잡음에도 불구하고 높은 결함진단 확률을 가질 수 있도록 하였다.

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.

키워드

참고문헌

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