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Meccanica

, Volume 41, Issue 3, pp 283–297 | Cite as

Fault Detection of DC Electric Motors Using the Bispectral Analysis

  • Miha Boltežar
  • Janko Slavič
Article

Abstract

The two major advantages of bispectral analysis are: resistance to noise and the ability to detect nonlinearities, like quadratic phase coupling. The first aim was to study some of the theoretical aspects of bispectral estimation. A lot of attention was paid to the influence of noise, the number of segments, the influence of one or several harmonic deterministic components and aliasing. These aspects are typical of rotating machinery. An example of successful fault identification in DC electric motors is presented. The identification proved to be capable to identify quadratically coupled mechanical system when the power-spectra analysis failed. Further it proved to be quite resistant to noise.

Keywords

Bispectral analysis Bicoherence DC electric motor Fault detection Condition monitoring 

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References

  1. 1.
    Boltežar, M., Hammond, J.K. 1999Experimental study of the vibrational behaviour of a coupled non-linear mechanical system’Mech. Syst. Signal Proce.13375394CrossRefADSGoogle Scholar
  2. 2.
    Hinich, M.J. 1982‘Testing for gaussianity and linearity of a stationary time series’J. Time Ser. Anal.3169176zbMATHMathSciNetGoogle Scholar
  3. 3.
    Fackrell, J.W.A. 1996Bispectral Analysis of Speech SignalsThe University of EdinburghUKPhD thesisGoogle Scholar
  4. 4.
    Hinich, M.J. and Wolinsky, M.A., ‘A test for aliasing using bispectral analysis’, J. Am. Statist. Assoc. 83(402) (June 1988) 499–502.Google Scholar
  5. 5.
    Zhang, G.C., Ge, M., Tong, H., Xu, Y. and Du, R., ‘Bispectral analysis for on-line monitoring of stamping operation’, Eng. Appl. Artif. Intel. 15(1) (Februar 2002) 97–104.Google Scholar
  6. 6.
    Yang, D.M., Stronach, A.F., MacConnell, P. 2003‘The application of advanced signal processing techniques to induction motor bearing condition diagnosis’Meccanica38297308zbMATHCrossRefGoogle Scholar
  7. 7.
    Wang, W.J., Wu, Z.T. and Chen, J., ‘Fault identification in rotating machinery using the correlation dimension and bispectra’, Nonlinear Dynam. 25(4) (August 2001) 383–393.Google Scholar
  8. 8.
    Kocur, D. and Stanko, R., ‘Order bispectrum: a new tool for reciprocated machine condition monitoring’, Mech. Syst. Signal Proce. 16(2–3) (Mar–May 2002) 391–411.Google Scholar
  9. 9.
    Jeffries, W.Q., Chambers, J.A. and Infield, D.G., ‘Experience with bicoherence of electrical power for condition monitoring of wind turbine blades’, IEE proce. Image signal proces 145(3) (Jun 1998) 141–148.Google Scholar
  10. 10.
    Simonovski, I., Boltežar, M., Gradišek, J., Govekar, E., Grabec, I., Kuhelj, A. 2002Bispectral analysis of the cutting process’Mech. Syst. Signal Proce.1611111122Google Scholar
  11. 11.
    Boltežar, M., Jakšić, N., Simonovski, I., Kuhelj, A., ‘Dynamical behaviour of the planar non-linear mechanical system – Part II: experiment’, J. Sound Vib. 226(5) (October 1999) 941–953.Google Scholar
  12. 12.
    Jakšić, N., Boltežar, M., Simonovski, I. and Kuhelj, A., ‘Dynamical behaviour of the planar non-linear mechanical system – Part I: theoretical modelling’, J. Sound Vib. 226(5) (October 1999) 923–940.Google Scholar
  13. 13.
    Simonovski, I., Uporaba spektrov tretjega reda pri analizi nelinearnih mehanskih nihanj (Third Order Spectra to the Analysis of Nonlinear Dynamical Systems). Master’s thesis, Fakulteta za strojništvo, Univerza v Ljubljani, 1998. In Slovene.Google Scholar
  14. 14.
    Elgar, S. and Sebert, G., ‘Statistics of Bicoherence and Biphase’, J. Geophys. Res. 94(C8) (August 1989) 10993–10998.Google Scholar
  15. 15.
    Chandran, V. and Elgar, S., ‘Mean and variance of estimates of the bispectum of a harmonic random process – an analysis including leakage effect’, IEEE Trans. Signals Proce. 39(12) (December 1991) 2640–2651, citeseer.nj.nec.com/207643.html.Google Scholar
  16. 16.
    Nikias, C.L. and Petropulu, A.P., Higher-order Spectral Analysis, Prentice-Hall, Inc., 1993.Google Scholar
  17. 17.
    Simonovski, I., Boltežar, M., Kuhelj, A. 1999‘Osnove bispektralne analize (Theoretical Background of the Bispectral Analysis)’Strojniški Vestnik-J. Mech. Eng.451224Google Scholar
  18. 18.
    Newland, D.E., An Introduction To Random Vibrations, Spectral And Wavelet Analysis, 3rd edn, Addison Wesley Longman Limited, 1993.Google Scholar
  19. 19.
    Haubrich, R.A., Earth Noise, 5–500 Milicycles per second’, J. Geophys. Res. 70(6) (March 1965) 1415–1427.Google Scholar
  20. 20.
    Boltežar, M., Simonovski, I., Furlan, M. 2003‘Fault detection in DC electro motors using the continuous wavelet transform’Meccanica38251264CrossRefGoogle Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  1. 1.Faculty of Mechanical EngineeringUniversity of LjubljanaLjubljanaSlovenia

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