The KSFM Journal of Fluid Machinery (한국유체기계학회 논문집)

Korean Society for Fluid machinery (한국유체기계학회)
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A Study on Discrete Hidden Markov Model for Vibration Monitoring and Diagnosis of Turbo Machinery

터보회전기기의 진동모니터링 및 진단을 위한 이산 은닉 마르코프 모델에 관한 연구

  • 이종민 (한국과학기술연구원 시스템연구부) ;
  • 황요하 (한국과학기술연구원 시스템연구부) ;
  • 송창섭 (한양대학교 기계공학부)
  • Published : 2004.04.01
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Abstract

Condition monitoring is very important in turbo machinery because single failure could cause critical damages to its plant. So, automatic fault recognition has been one of the main research topics in condition monitoring area. We have used a relatively new fault recognition method, Hidden Markov Model(HMM), for mechanical system. It has been widely used in speech recognition, however, its application to fault recognition of mechanical signal has been very limited despite its good potential. In this paper, discrete HMM(DHMM) was used to recognize the faults of rotor system to study its fault recognition ability. We set up a rotor kit under unbalance and oil whirl conditions and sampled vibration signals of two failure conditions. DHMMS of each failure condition were trained using sampled signals. Next, we changed the setup and the rotating speed of the rotor kit. We sampled vibration signals and each DHMM was applied to these sampled data. It was found that DHMMs trained by data of one rotating speed have shown good fault recognition ability in spite of lack of training data, but DHMMs trained by data of four different rotating speeds have shown better robustness.

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References

  1. 송성진, 2003, '컨디션 모니터링이 최신 가스터빈에 마치는 영향' 유체기계저널, 제 6권, 제 1 호, pp. 131~ 136
  2. Rabiner, L. R., 1989, 'A Tutorial on Hidden Markov Models and Selected Application in Speech Recognition,' Proceedings of the IEEE, Vol 77, No.2, pp. 257-286
  3. 이종민, 김승종, 황요하, 송창섭, 2003, 'AR 계수를 이용한 Hidden Markov Model의 기계 상태진단 적용,' 한국소음진동공학회논문집, 제13권, 제1호, pp. 48-55
  4. Wong, J, C., McDonald, K. A. and Pallazoglu, A., 2001, 'Classification Abnormal PlantOeration using Multiple Process Variable Trends,' Journal of Process Control, Vol. 11, pp. 409-418 https://doi.org/10.1016/S0959-1524(00)00011-1
  5. Kwon, K -C. and Kim, J,-H., 1999, 'Accident Identification in Nuclear Power Plants using Hidden Markov Models,' Engineering Applications of Artificial Intelligence, Vol. 12, pp. 491-501 https://doi.org/10.1016/S0952-1976(99)00011-1
  6. Bunks , C., McCarthy, D. and Al- Ani, T., 2000, 'Condition- based Maintenance of Machines using Hidden Markov Models,' Mechanical Systems and Signal Processing, Vol. 14, pp.597-612 https://doi.org/10.1006/mssp.2000.1309
  7. Ertunc , H. M., Loparo, K A. and 0cak, H., 2001, 'Tool Wear Condition Monitoring in Drilling Operations using Hidden Markov Models,' International Journal of Machine Tools and Manufacture, Vol. 41, pp. 1363- 1384 https://doi.org/10.1016/S0890-6955(00)00112-7
  8. Lee, J. M., Kim, S.-J, Hwang, Y. and Song, C.-S., 2003 , 'Condition Monitoring of Rotor Fault Signal using Hidden Markov Model,' The 32nd International Congree and Exposition on Noise Control Engineering, pp. 3138-3145(CD-ROM)
  9. 이종민, 김승종, 황요하, 송장섭, 2003, '은닉 마르코프 모형을 이용한 회전제 결함선호의 패턴 인식', 대한기계학회논문집 A권, 제27권, 제11호, pp. 1864-1872
  10. Rabiner, L. R. and Jaung, B.-H., 1993, FAUNDAMENTALS OF SPEECH RECOGNITION, Prentice Hall Inc., New Jersey U.S.A., pp. 69-140 & pp. 321-389
  11. Gray, R. M., 1984, 'Vector Quantization,' IEEE ASSP Magazine, pp. 4 -28
  12. Rao, J. S., 2000, VIERATORY CONDITION MONITORING OF MACHINES, Narosa Publishing House, New Delhi India, pp. 312-382
  13. 정보통신연구회, 2001, 우도의 이해, 교우사, 서울 대한민국, pp. 7-22