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베어링 결함이 있는 회전기계의 상태 진단을 위한 은닉 마르코프 모델의 적용Application of Hidden Markov Model to Condition Monitoring of Rotating Machine with Mass Unbalance
- Other Titles
- Application of Hidden Markov Model to Condition Monitoring of Rotating Machine with Mass Unbalance
- Issue Date
- May-2015
- Publisher
- 대한기계학회
- Keywords
- Hidden Markov Model; HMM, 마르코프 모델; Fault Diagnosis; 결함 진단; Feature Vector; 특징벡터; Vector Quantization; 벡터 양자화; Bearing fault; 베어링 결함
- Citation
- 대한기계학회 2015년도 동역학 및 제어부문 춘계학술대회 논문집, pp.117 - 118
- Indexed
- OTHER
- Journal Title
- 대한기계학회 2015년도 동역학 및 제어부문 춘계학술대회 논문집
- Start Page
- 117
- End Page
- 118
- Abstract
- In recent research, pattern recognition method has been widely used by many researchers for fault diagnoses of mechanical systems. Also it determines the soundness of a mechanical system by detecting variations in the systems's vibration characteristics. Hidden Markov model (HMM) has recently been used as pattern recognition methods in various fields. In this study, a HMM method for the fault diagnosis of a rotating machine with bearing fault is introduced. The existence, location, and quantity of bearing fault are identified. Also Fast Fourier Transform (FFT) is employed to extract feature vector.
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Collections - 서울 공과대학 > 서울 기계공학부 > 1. Journal Articles
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