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초록
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.
키워드
Hidden Markov Model; HMM, 마르코프 모델; Fault Diagnosis; 결함 진단; Feature Vector; 특징벡터; Vector Quantization; 벡터 양자화; Bearing fault; 베어링 결함
- 제목
- 베어링 결함이 있는 회전기계의 상태 진단을 위한 은닉 마르코프 모델의 적용
- 제목 (타언어)
- Application of Hidden Markov Model to Condition Monitoring of Rotating Machine with Mass Unbalance
- 저자
- 고정민; 최찬규; 강토; 한순우; 박진호; 유홍희
- 발행일
- 2015-05
- 유형
- Proceeding
- 저널명
- 대한기계학회 2015년도 동역학 및 제어부문 춘계학술대회 논문집
- 페이지
- 117 ~ 118