Browse > Article
http://dx.doi.org/10.5050/KSNVE.2012.22.5.413

Condition Monitoring of Rotating Machine with a Change in Speed Using Hidden Markov Model  

Jang, M. (고려대학교 기계공학부)
Lee, J.M. (한국과학기술연구원)
Hwang, Y. (한국과학기술연구원)
Cho, Y.J. ((주)두산모트롤)
Song, J.B. (고려대학교 기계공학부)
Publication Information
Transactions of the Korean Society for Noise and Vibration Engineering / v.22, no.5, 2012 , pp. 413-421 More about this Journal
Abstract
In industry, various rotating machinery such as pumps, gas turbines, compressors, electric motors, generators are being used as an important facility. Due to the industrial development, they make high performance(high-speed, high-pressure). As a result, we need more intelligent and reliable machine condition diagnosis techniques. Diagnosis technique using hidden Markov-model is proposed for an accurate and predictable condition diagnosis of various rotating machines and also has overcame the speed limitation of time/frequency method by using compensation of the rotational speed of rotor. In addition, existing artificial intelligence method needs defect state data for fault detection. hidden Markov model can overcome this limitation by using normal state data alone to detect fault of rotational machinery. Vibration analysis of step-up gearbox for wind turbine was applied to the study to ensure the robustness of diagnostic performance about compensation of the rotational speed. To assure the performance of normal state alone method, hidden Markov model was applied to experimental torque measuring gearbox in this study.
Keywords
Hidden Markov Model; Rotating Machine; Condition Monitoring; Compensation of Rotational Speed;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Yang, B. S., Hwang, W. W., Kim, D. J. and Tan, A., 2005, Condition Classification of Small Reciprocating Compressor for Refrigerators using Artificial Neural Networks and Support Vector Machines, Mechanical Systems and Signal Processing, Vol. 19, No. 2, pp. 371-390.   DOI
2 Chae, H. C., Ryu, I. C. and Han, C. S., 2003, 3-Dimensional Modeling and Sensitivity Analysis for Vibration Reduction of the Spin-coater System(in Korean), Journal of the Korean Society of Precision Engineering. Vol. 20, No. 2, pp. 209-217.
3 Tomasz, B. and Robert, B. R., 2009, Application of Spectral Kurtosis for Detection of a Tooth Crack in the Planetary Gear of a Wind Turbine, Mechanical Systems and Signal Processing, Vol. 23, pp. 1352-1365.   DOI
4 Boulahbal, D., Golnaraghi, M. F. and Ismail, F., 1999, Amplitude and Phase Wavelet Maps for the Detection of Cracks in Geared Systems, Mechanical Systems and Signal Processing, Vol. 13, pp. 423-436.   DOI   ScienceOn
5 Han, H. S., Cho, S. J. and Chong, U. P., 2011, Neural-network-based Fault Detection and Diagnosis Method Using EIV(errors-in variables), Journal of Sound and Vibration, Vol. 21, No. 11, pp. 1020-1028.
6 Bunks, C., McCarthy, D. and Al-Ani, T., 2000, Condition-based Maintenance of Machine Using Hidden Markov Models, Mechanical Systems and Signal Processing, Vol. 14, pp. 597-612.   DOI   ScienceOn
7 Ertunc, H. M., Loparo, K. A. and Ocak, 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.   DOI   ScienceOn
8 Lee, J. M., Kim, S. J., Hwang, Y. and Song, C. S., 2004, Diagnosis of Mechanical Fault Signals Using Continuous Hidden Markov Model, Journal of Sound and Vibration. Vol. 276, pp. 1065-1080.   DOI
9 Lee, J. M., Kim, S. J., Hwang, Y. and Song, C. S., 2003, Pattern Recognition of Rotor Fault Signal Using Hidden Markov Model, Journal of Korean Society of Mechanical Engineering, Vol. 27, No. 11, pp. 1864-1872.   DOI   ScienceOn
10 Lee, J. M. and Hwang, Y., 2011, New Machine Condition Diagnosis Method Not Requiring Fault Data Using Continuous Hidden Markov Model, Transactions of the Korean Society for Noise and Vibration Engineering, Vol. 21, No. 2, pp. 146-153.   DOI
11 Zhang, G., Ge, Y., Fang, K. and Liang, Q., 2008, An Intelligent Monitor System for Gearbox Test, Communications in Computer and Information Science, Vol. 15, Part. 7, pp. 252-259.   DOI
12 Zhan, Y. and Makis, V., 2006, A Robust Diagnostic Model for Gearboxes Subject to Vibration Monitoring, Journal of Sound and Vibration, Vol. 290, No. 3-5, pp. 928-955.   DOI