HMM/AMM 복합 모델을 이용한 회전 블레이드의 결함 진단
- Title
- HMM/AMM 복합 모델을 이용한 회전 블레이드의 결함 진단
- Other Titles
- AMM 복합 모델을 이용한 회전 블레이드의 결함 진단
- Author
- 유홍희
- Keywords
- 은닉 마르코프 모델; 인공 신경망; 결함 진단; 특징벡터; 벡터 양자화; Hidden Markov Model(HMM); Artificial Neural Network(ANN); Fault Diagnosis; Feature Vector; Vector Quantization
- Issue Date
- 2013-09
- Publisher
- 한국소음진동공학회
- Citation
- 한국소음진동공학회 논문집, 2013, 23(9), P.814-822
- Abstract
- 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.
- URI
- http://koreascience.or.kr/article/ArticleFullRecord.jsp?cn=SOJDCM_2013_v23n9_814
- ISSN
- 1598-2785; 2287-5476
- DOI
- 10.5050/KSNVE.2013.23.9.814
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > MECHANICAL ENGINEERING(기계공학부) > Articles
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