이종민 김승종 황요하 송창섭 2024-01-21T08:04:59Z 2024-01-21T08:04:59Z 2021-09-06 2003-11 1226-4873 https://pubs.kist.re.kr/handle/201004/138117 Hidden Markov Model(HMM) has been widely used in speech recognition, however, its use in machine condition monitoring has been very limited despite its good potential. In this paper, HMM is used to recognize rotor fault pattern. First, we set up rotor kit under unbalance and oil whirl conditions. Time signals of two failure conditions were sampled and translated to auto power spectrums. Using filter bank, feature vectors were calculated from these auto power spectrums. Next, continuous HMM and discrete HMM were trained with scaled forward/backward variables and diagonal covariance matrix. Finally, each HMM was applied to all sampled data to prove fault recognition ability. It was found that HMM has good recognition ability despite of small number of training data set in rotor fault pattern recognition. Korean 대한기계학회 은닉 마르코프 모형을 이용한 회전체 결함신호의 패턴 인식 Pattern Recognition of Rotor Fault Signal Using Hidden Markov Model Article 2 대한기계학회논문집 A, v.27, no.11, pp.1864 - 1872 대한기계학회논문집 A 27 11 1864 1872 kci ART000995951 Hidden Markov Model(HMM) 은닉 마르코프 모형 rotor fault signal 회전체 결함신호 machine diagnosis 기계 진단 unbalance 불평형 oil whirl 오일 휠