Rolling Bearing Diagnosis Based on the Higher Order Spectra



Published Nov 10, 2020
Len Gelman Tejas H. Patel Brian Murray Allan Thomson


Bearing defect diagnosis is traditionally done using the demodulation/enveloping technology. Diagnosis is mostly based on the spectrum of the squared envelope signal. In literature, the use of the higher order spectra (HOS) has shown to have a tremendous potential for vibration based diagnostics. In this paper we implemented and experimentally validated the higher order spectra based on the envelope analysis for the diagnosis of ball bearing defects. The implemented technology employs the spectral kurtosis to obtain a frequency band for the demodulation and the third order normalized spectra, i.e. the bicoherence for diagnosis of bearing fault. The high effectiveness of the diagnostics of the implemented technology has been experimentally revealed and compared with that of well-known demodulation/enveloping technology.

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vibration analysis, the spectral kurtosis, bearing defect diagnosis, the bicoherence of the defect frequencies, the envelope analysis

McFadden, P., and Smith, J. (1984). Vibration monitoring of rolling element bearings by the high frequency resonance technique – a review. Tribology International, vol. 17, pp. 3-10. doi:10.1016/0301-679X(84)90076-8
Tandon, N., and Choudhury, A. (1999). A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, vol. 33, pp. 469-480. doi: 10.1016/S0301-679X(99)00077-8
Randall, R., and Antoni, J. (2011). Rolling element bearing diagnostics – A tutorial. Mechanical Systems and Signal Processing, vol. 25, pp. 485–520. doi: 10.1016/j.ymssp.2010.07.017
Wang, P., Youn, B.D., and Hu, C. (2012). A generic orobabilistic framework for structural health prognostic and uncertainty management. Mechanical Systems and Signal Processing, vol. 28, pp. 622–637. doi: 10.1016/j.ymssp.2011.10.019
Camci, F., Medjaher, K., Zerhouni, N., and Nectoux, P. (2013). Feature evaluation for effective bearing prognostics. Quality and Reliability Engineering International, vol. 29, pp. 477–486. doi: 10.1002/qre.1396
Hu, C., Youn, B.D., and Wang, P. (2012). Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life. Reliability Engineering and System Safety, vol. 103, pp. 120–135. doi: 10.1016/j.ress.2012.03.2008
Medjaher, K., Camci, F., and Zerhouni, N. (2012). Feature extraction and evaluation for health assessment and failure prognostics. First European Conference of the Prognostics and Health Management Society, Dresden, Germany, July 3-5, 2012.
Sawalhi, N., and Randall, R. (2004). The application of spectral kurtosis to bearing diagnostics. Proceedings of ACOUSTICS, November 3-5, Gold Coast, Australia, pp. 393-398.
Antoni, J. (2006). The spectral kurtosis: A useful tool for characterizing non-stationary signals. Mechanical Systems and Signal Processing, vol. 20, pp. 282–520. doi:10.1016/j.ymssp.2004.09.001
Nikolaou, N.G., and Antoniadis, I.A. (2002). Demodulation of vibration signals generated by defects in rolling element bearings using complex shifted Morlet wavelets. Mechanical Systems and Signal Processing, vol. 16, pp. 677–694. doi:10.1006/mssp.2001.1459.
Wang, Y., and Liang, M. (2011). An adaptive SK technique and its application for fault detection of rolling element bearings. Mechanical Systems and Signal Processing, vol. 25, pp. 1750–1764. doi:10.1016/j.ymssp.2010.12.008
Howard, I. (1997). Higher order spectral techniques for machine vibration condition monitoring. Proc. of IMechE - Part G, vol. 211, pp. 211-219. doi: 10.1243/0954410971532622
Jiang, L., Liu, Y., Li, X., and Tang, S. (2011). Using bispectral distribution as a feature for rotating machinery fault diagnosis. Measurement, vol. 44, pp. 1284-1292. doi: 10.1016/j.measurement.2011.03.024
Gelman, L., White, P., and Hammond, J. (2005). Fatigue crack diagnostics: A comparison of the use of the complex bicoherence and its magnitude. Mechanical Systems and Signal Processing, vol. 19, pp. 913–918. doi: 10.1016/j.ymssp.2004.07.009
Li, C., Ma, J., and Hwang, B. (1996). Bearing condition monitoring by pattern recognition based on bicoherence analysis of vibrations. Proc. of IMechE - Part C, vol. 210, pp. 277-285. doi: 10.1243/PIME_PROC_1996_210_197_02
Yang, D., Stronach, A., and MacConnell, P. (2002). Third order spectral techniques for the diagnosis of motor bearing condition using artificial neural networks. Mechanical Systems and Signal Processing, vol. 16, pp. 391–411. doi: 10.1006/mssp.2001.1469
Webb A. (2003). Statistical Pattern Recognition, Johan Wiley & Sons Ltd.
Gelman, L., Lapena, E., and Thompson, C. (2009). Advanced higher order spectra for classification of damage in transient conditions. Journal of Intelligent Material Systems and Structures, vol. 20, pp. 1343-1349. doi: 10.1177/1045389X08097383
Collis, W., White, P., and Hammond, J. (1998). Higher order spectra: Bispectrum and Trispectrum. Mechanical Systems and Signal Processing, vol. 12, pp. 375-394. doi: 10.1006/mssp.1997.0145
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