New Machine Condition Diagnosis Method Not Requiring Fault Data Using Continuous Hidden Markov Model

Title
New Machine Condition Diagnosis Method Not Requiring Fault Data Using Continuous Hidden Markov Model
Authors
이종민황요하
Keywords
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
2011-02
Publisher
한국소음진동공학회논문집
Citation
VOL 21, NO 2, 146-153
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.
URI
http://pubs.kist.re.kr/handle/201004/39476
ISSN
1598-2785
Appears in Collections:
KIST Publication > Article
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