New Machine Condition Diagnosis Method Not Requiring Fault Data Using Continuous Hidden Markov Model
- New Machine Condition Diagnosis Method Not Requiring Fault Data Using Continuous Hidden Markov Model
- 이종민; 황요하
- Machine Condition Diagnosis; Hidden Markov Model; Pattern Recognition; Failure Detection; Weld Defect; 기계상태 진단; HMM, 은닉 마르코프 모델; 패턴인식; 결함 감지; 용접 결함
- Issue Date
- VOL 21, NO 2, 146-153
- 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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- KIST Publication > Article
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