Advanced SearchSearch Tips
Fault Diagnosis of Rotating System Mass Unbalance Using Hidden Markov Model
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Fault Diagnosis of Rotating System Mass Unbalance Using Hidden Markov Model
Ko, Jungmin; Choi, Chankyu; Kang, To; Han, Soonwoo; Park, Jinho; Yoo, Honghee;
  PDF(new window)
In recent years, pattern recognition methods have been widely used by many researchers for fault diagnoses of mechanical systems. The soundness of a mechanical system can be checked by analyzing the variation of the system vibration characteristic along with a pattern recognition method. Recently, the hidden Markov model has been widely used as a pattern recognition method in various fields. In this paper, the hidden Markov model is employed for the fault diagnosis of the mass unbalance of a rotating system. Mass unbalance is one of the critical faults in the rotating system. A procedure to identity the location and size of the mass unbalance is proposed and the accuracy of the procedure is validated through experiment.
Hidden Markov Model;Fault Diagnosis;Feature Vector;Vector Quantization;Mass Unbalance;Rotating System;
 Cited by
Martin, K. F., 1994, A Review by Discussion of Monitoring and Fault-diagnosis in Machine-tools, International Journal of Machine Tools and Manufacture, Vol. 34, No. 4, pp. 527~551. crossref(new window)

Rabiner, L. R., 1989, A Tutorial on Hidden Markov Models and Selected Application in Speech Recognition, Proc, IEEE, Vol. 77, No. 2, pp. 257~286. crossref(new window)

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, No. 4, pp. 597~612. crossref(new window)

Kim, J. S. and Yoo, H. H., 2013, Fault Diagnosis of a Rotating Blade using HMM/ANN Hybrid Model, Transactions of the Korean Society for Noise and Vibration Engineering, Vol. 23, No. 9, pp. 815~816.

Liu, Z., Yin, X., Zhang, Z., Chen, D. and Chen, W., 2004, Online Rotor Mixed Fault Diagnosis Way Based on Spectrum Analysis of Instantaneous Power in Squirrel Cage Induction Motors, IEEE Transactions on Energy Conversion, Vol. 19, No. 3, pp. 485~490. crossref(new window)

Robert, M. G., 1984, Vector Quantization, IEEE ASSP Magazine, pp. 4~28.

Fan, G. and Xia, X.-G., 2001, Improved Hidden Markov Models in the Wavelet-domain, IEEE Transactions on Signal Processing, Vol. 49, No. 1, pp. 115~120. crossref(new window)

Lee, J. M., Kim, S. J., Hwang, Y. H. and Song, C. S., 2003, Pattern Recognition of Rotor Fault Signal Using Hidden Markov Model, Journal of the KSME, Vol. 27, No. 11, pp. 1864~1872.