Refining Envelope Analysis Methods usingWavelet De-Noising to Identify Bearing Faults

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Jul 8, 2014
Edward Max Bertot Pierre-Philippe Beaujean David Vendittis

Abstract

In the field of machine health monitoring, vibration analysis is a proven method for detecting and diagnosing bearing faults in rotating machines. One popular method for interpreting vibration signals is envelope-demodulation, which allows the maintainer to clearly identify an impulsive fault source and its severity. In some cases, in-band noise can make impulses associated with incipient faults difficult to detect and interpret. In this paper, we use Wavelet De-Noising (WDN) after envelope-demodulation to improve the accuracy of bearing fault diagnostics. This contrasts the typical approach of de-noising raw vibration signals prior to demodulation. We find that WDN removes low amplitude harmonics and spurious reflections which may interfere with FFT techniques to identify low-frequency peaks in the signal spectrum. When measuring impact frequencies in the time-domain using a peakthresholding method, the proposed algorithm exhibits increasingly confident periodicity at bearing fault frequencies.

How to Cite

Bertot, E. M., Beaujean, P.-P., & Vendittis, D. (2014). Refining Envelope Analysis Methods usingWavelet De-Noising to Identify Bearing Faults. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1521
Abstract 487 | PDF Downloads 99

##plugins.themes.bootstrap3.article.details##

Keywords

bearing defect diagnosis, wavelet de-noising

References
Bearing data center. (2013). Case Western Reserve University. Retrieved Jan 2014, from http://csegroups.case.edu/bearingdatacenter/
Bozchalooi, I. S., & Liang, M. (2007, July). A combined spectral subtraction and wavelet de-noising method for bearing fault diagnosis. IEEE Proceedings of the 2007 American Control Conference, 2533-2538.
Daubechies, I. (1992). Ten lectures on wavelets. Philadelphia, PA: Society for Industrial and Applied Mathematics.
Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(3), 613-627.
Donoho, D. L., & Johnstone, I. M. (1994). Ideal spatial adaptation via wavelet shrinkage. Biometrika, 81, 425-455.
McFadden, P., & Smith, J. (1984). Model for the vibration produced by a single point defect in a rolling element bearing. Journal of Sound and Vibration, 96, 69-82.
Qui, H., Lee, J., Lin, J., & Yu, G. (2006). Wavelet filter-based weak signature detection method and its application on roller element bearing prognostics. Journal of Sound and Vibration, 289, 1066-1090.
Rioul, O., & Vetterli, M. (1991, October). Wavelets and signal processing. IEEE Signal Processing Magazine, 8(4), 14-38.
Waters, N., & Beaujean, P. (2013). Targeting faulty bearings for an ocean turbine dynamometer. International Journal of Prognostics and Health Monitoring, 4, 1-15.
Section
Technical Papers

Similar Articles

<< < 6 7 8 9 10 11 12 13 14 > >> 

You may also start an advanced similarity search for this article.