Journal of Mechanical Science and Technology

The Korean Society of Mechanical Engineers (대한기계학회)
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Diagnostics and Prognostics Based on Adaptive Time-Frequency Feature Discrimination

  • Oh, Jae-Hyuk (School of Mechanical and Aerospace Engineering, Seoul National University) ;
  • Kim, Chang-Gu (School of Mechanical and Aerospace Engineering, Seoul National University) ;
  • Cho, Young-Man (School of Mechanical and Aerospace Engineering, Seoul National University)
  • Published : 2004.09.01
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Abstract

This paper presents a novel diagnostic technique for monitoring the system conditions and detecting failure modes and precursors based on wavelet-packet analysis of external noise/vibration measurements. The capability is based on extracting relevant features of noise/vibration data that best discriminate systems with different noise/vibration signatures by analyzing external measurements of noise/vibration in the time-frequency domain. By virtue of their localized nature both in time and frequency, the identified features help to reveal faults at the level of components in a mechanical system in addition to the existence of certain faults. A prima-facie case is made via application of the proposed approach to fault detection in scroll and rotary compressors, although the methods and algorithms are very general in nature. The proposed technique has successfully identified the existence of specific faults in the scroll and rotary compressors. In addition, its capability of tracking the severity of specific faults in the rotary compressors indicates that the technique has a potential to be used as a prognostic tool.

Keywords

References

  1. Cann, R. G., 1992, 'New Diagnostic Procedures for Locating Faults in Diesel Engines', Diesel and Gas Turbine Worldwide, Vol. 24, No. 6, pp. 52-55, July-August
  2. Choi, H. I. and Williams, W. J., 1989, 'Improved Time-Frequency Representation of Multi-Component Signals Using Exponential Kernels', IEEE Trans. Acoust., Speech, and Signal Proc., Vol. 37, No.6 https://doi.org/10.1109/ASSP.1989.28057
  3. Coifman, R. R. and Wickerhauser, M. V., 1992, 'Entropy-Based Algorithms for Best Basis Selection', IEEE Trans. on Inf. Theory, Vol. 38, No.2 https://doi.org/10.1109/18.119732
  4. Coifman, R. R. and Wickerhauser, M. V., 1993, 'Wavelet Packet Laboratory for Windows User's Manual & Theoretical Manual', Digital Diagnostics Corporation, A K Peters, Ltd., ISBN 1-56881-035-0
  5. Daubechies, I., 1992, 'Ten Lectures on Wavelets', SIAM, ISBN 0-89871-274-2
  6. Golub, G. H. and Van Loan, C. F., 1996, 'Matrix Computations', The Johns Hopkins University Press, Baltimore and London
  7. Harrap, M. J. and Wang, W. Y., 1994, 'Vibration Analysis Techniques Used if Machinery Fault Detection', Acoustics Australia, Vol. 22, No.3, pp. 91-95, December
  8. Kim, Y. Y. and Kim, E. H., 2000, 'A new Damage Detection Method Based on a Wavelet Transform', IMAC-XVIII: a conference on structural dynamics, San Antonio, TX, February
  9. Mallat, S., 1998, 'A wavelet Tour of Signal Processing', Academic Press, New York
  10. Meng, Q. and Qu, L., 1991, 'Rotating Machinery Fault Diagnosis Using Wigner Distribution', Mechanical Systems and Signal Processing, Vol. 5, No.3, pp. 155-166, May https://doi.org/10.1016/0888-3270(91)90040-C
  11. Misti, M., et al., 1996, Wavelet Toolbox, The Math Works, Inc.
  12. Park, J., 2000, 'Diagnosis of Excessive Vibration Signals of Two-Pole Generator Rotors in Balancing', KSME International Journal, Vol. 14, No.6, pp. 590- 596
  13. Park, J. and Lee, C., 1999, 'Diagnosis of Faults in Rolling Element Bearings by Using Directional Spectra of Vibration Signals', KSME International Journal, Vol. 13, No.1, pp.63-73
  14. Saito, N., 1994, 'Local Feature and Its Application Using a Library of Basis', Ph. D. Thesis, Yale University
  15. Thompson, R. B., 1983, 'Quantitative Ultrasonic Non-Destructive Evaluation Methods', ASME Trans. J. of Applied Mechanics, Vol. 50
  16. Shim, Mun-Bo and Suh, Myung-Won., 2002, 'Crack Identification Using Neuro- Fuzzy-Evolutionary Technique' KSME International Journal, Vol. 16 No.4, pp.454-467
  17. Tahk, K. and Shin, K., 2002, 'A Study on the Fault Diagnosis of Roller-Shape Using Frequency Analysis of Tension Signals and Artificial Neural Networks Based Approach in a Web Transport System' KSME International Journal, Vol. 16 No. 12, pp. 1604-1612