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

The Korean Society for Noise and Vibration Engineering (한국소음진동공학회)
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New Machine Condition Diagnosis Method Not Requiring Fault Data Using Continuous Hidden Markov Model

결함 데이터를 필요로 하지 않는 연속 은닉 마르코프 모델을 이용한 새로운 기계상태 진단 기법

  • 이종민 (한국과학기술연구원 에너지메카닉스센터) ;
  • 황요하 (한국과학기술연구원 에너지메카닉스센터)
  • Received : 2010.11.29
  • Accepted : 2011.01.17
  • Published : 2011.02.20
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Abstract

Model based machine condition diagnosis methods are generally using a normal and many failure models which need sufficient data to train the models. However, data, especially for failure modes of interest, is very hard to get in real applications. So their industrial applications are either severely limited or impossible when the failure models cannot be trained. In this paper, continuous hidden Markov model(CHMM) with only a normal model has been suggested as a very promising machine condition diagnosis method which can be easily used for industrial applications. Generally hidden Markov model also uses many pattern models to recognize specific patterns and the recognition results of CHMM show the likelihood trend of models. By observing this likelihood trend of a normal model, it is possible to detect failures. This method has been successively applied to arc weld defect diagnosis. The result shows CHMM's big potential as a machine condition monitoring method.

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References

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