Abstract
The diagnosis of bearing health through the quantification of accelerometer data has been an area of interest for many years and has resulted in numerous signal processing methods and algorithms. This paper proposes a new diagnostic approach that combines envelope analysis, time synchronous resampling, and spectral averaging of vibration signals to extract condition indicators (CIs) used for rolling-element bearing fault diagnosis. First, the accelerometer signal is digitized simultaneously with tachometer signal acquisition. Then, the digitized vibration signal is band pass filtered to retain the information associated with the bearing defects. Finally, the tachometer signal is used to time synchronously resample the vibration data which allows the computation of a spectral average and the extraction of the CIs used for bearing fault diagnosis. The proposed technique is validated using the vibration output of seeded fault steel bearings on a bearing test rig. The result is an effective approach validated to diagnose all four bearing fault types: inner race, outer race, ball, and cage.
Similar content being viewed by others
References
Applied Industrial Technologies, 2009, Lubricant failure = bearing failure. http://www.machinerylubrication.com/Magazine/Issue/Machinery%20Lubrication/1/2009
E. Bechhoefer, M. Kingsley, A review of time synchronous average algorithms. Annual Conference of the Prognostics and Health Management Society, Sept 27–Oct 1, (San Diego, CA, 2009)
E. Bechhoefe, An enhanced time synchronous averaging for rotating equipment analysis. Proceedings for the joint conference: Machinery Failure Prevention Technology 2013 and International Instrumentation Symposium 2013, May 13–17, Cleveland, OH, 2013
E. Bechhoefer, B. Van Hecke, D. He, Processing for improved spectral analysis. Annual Conference of the Prognostics and Health Management Society, Oct 14–17, New Orleans, LA, 2013
F. Bonnardot, El M. Badaoui, R.B. Randall, J. Daniere, F. Guillet, Use of the acceleration signal of a gearbox in order to perform angular resampling (with limited speed fluctuation). Mech. Syst. Signal Process. 19(4), 766–785 (2005)
S. Braun, The extraction of periodic waveforms by time domain averaging. Acustica 32, 69–77 (1975)
K.N. Christian, N. Mureithi, A. Lakis, M. Thomas, On the use of synchronous averaging, independent component analysis and support vector machines for bearing fault diagnosis. First International Conference on Industrial Risk Engineering, Dec 17–19 Montreal, QC, Canada, 2007
D. Felten, Understanding Bearing Vibration Frequencies, (Mechanical Field Service Department, L&S Electric, Inc., Schofield, Wisconsin, 2003), pp. 1–3
D. He, R. Li, J. Zhu, Plastic bearing fault diagnosis based on a two-step data mining approach. IEEE Trans. Indus. Electron. 60(8), 3429–3440 (2012)
P.D. McFadden, A revised model for the extraction of periodic waveforms by time domain averaging. Mech. Syst. Signal Process. 1(1), 83–95 (1987)
P.D. McFadden, A technique for calculating the time domain averages of the vibration of the individual planet gears and the sun gear in an epicyclic gearbox. J. Sound Vib. 144(1), 163–172 (1991)
P.D. McFadden, M.M. Toozhy, Application of synchronous averaging to vibration monitoring of rolling element bearings. Mech. Syst. Signal Process. 14(6), 891–906 (2000)
Y. Qu, E. Bechhoefer, D. He, J. Zhu, A new acoustic emission sensor based gear fault detection approach. Int. J. Progn. Health Manag. 4(Sp. 2), 1–14 (2013)
Y. Qu, J. Zhu, D. He, B. Qiu, E. Bechhoefer, Time synchronous average based acoustic emission signal analysis on gear fault detection. IEEE International Conference on Prognostics and Health Management, June 24–27, Gaithersburg, MD, 2013
J. Shiroishi, Y. Li, S. Liang, T. Kurfess, S. Danyluk, Bearing condition diagnosis via vibration and acoustic emission measurements. Mech. Syst. Signal Process. 11(5), 693–705 (1997)
D. Siegel, H. Al-Atat, V. Shauche, L. Liao, J. Snyder, J. Lee, Novel method for rolling element bearing health assessment—a tachometer-less synchronously averaged envelope feature extraction technique. Mech. Syst. Signal Process. 29(1), 362–376 (2012)
H.M. Teager, S.M. Teager, Evidence for nonlinear sound production mechanisms in the vocal tract. Speech Production and Speech Modeling Symposium, Time Frequency and Time-Scale Analysis, Victoria, BC, Canada, 1992, pp. 345–348
P. Welch, The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15(2), 70–73 (1967)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Van Hecke, B., Qu, Y., He, D. et al. A New Spectral Average-Based Bearing Fault Diagnostic Approach. J Fail. Anal. and Preven. 14, 354–362 (2014). https://doi.org/10.1007/s11668-014-9806-6
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11668-014-9806-6