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All issues Volume 14 / No 2 (2013) Mechanics & Industry, 14 2 (2013) 129-136 References
Open Access
Issue
Mechanics & Industry
Volume 14, Number 2, 2013
Page(s) 129 - 136
DOI https://doi.org/10.1051/meca/2013060
Published online 12 June 2013
  1. B.T. Kuhnell, Wear in rolling element bearings and gears – how age and contamination affect them, Machinery Lubrication Magazine, Monash University, 2004 [Google Scholar]
  2. N. Tandon, B.C. Nakra, Detection of defects in rolling element bearings by vibration monitoring, J. Institution of Engineers (India)- Mechanical Engineering Division (ISSN 0020-3408), 73 (1993) 271–282 [Google Scholar]
  3. R.A Collacott, Mechanical fault diagnosis, Chapman and Hall, London, 1977 [Google Scholar]
  4. K.S. Jardine, D. Lin, D. Banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mech. Syst. Signal Process. 20 (2006) 1483–1510 [Google Scholar]
  5. N. Tandon, A. Choudury, A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings, Tribol. Int. 32 (1999) 469–480 [Google Scholar]
  6. PD. McFadden, J.D. Smith Vibration monitoring of rolling element bearings by the high frequency resonance technique – a review. Tribol. Int. 17 (1984) 3–10 [Google Scholar]
  7. PD. McFadden, J.D. Smith, Model for the vibration produced by a single point defect in a rolling element bearing, J. Sound Vibr. 96 (1984) 69–82 [Google Scholar]
  8. PD. McFadden, J.D. Smith, The vibration produced by multiple point defects in a rolling element bearing, J. Sound Vibr. 98 (1985) 263–273 [Google Scholar]
  9. Z.K. Zhu, R.Q. Yan, L.H. Luo, Z.H. Feng, F.R. Kong, Detection of signal transients based on wavelet and statistics for machine fault diagnosis, Mech. Syst. Signal Process. 23 (2009) 1076–1097 [CrossRef] [Google Scholar]
  10. R.B. Randall, J. Antoni, Rolling element bearing diagnostics-A tutorial, Mech. Syst. Signal Process. 25 (2011) 485–520 [Google Scholar]
  11. J. Antoni, Cyclic spectral analysis in practice, Mech. Syst. Signal Process. 21 (2007) 597–630 [CrossRef] [Google Scholar]
  12. H. Wang, P. Chen, Fuzzy diagnosis method for rotating machinery in variable rotating Speed, IEEE Sensors J. 11 (2011) 23–34 [Google Scholar]
  13. W. Bartelmus, R. Zimroz, A new feature for monitoring the condition of gearboxes in non-stationary operating conditions, Mech. Syst. Signal Process. 23 (2009) 1528–1534 [CrossRef] [Google Scholar]
  14. J. McBain, M. Timusk, Fault detection in variable speed machinery: Statistical parameterization, J. Sound Vibr. 237 (2009) 623–646 [Google Scholar]
  15. N. Tandon, A. Choudury, An analytical model for the prediction of the vibration response of rolling element bearings due to localized defect, J. Sound Vibr. 205 (1997) 275–292 [Google Scholar]
  16. C. Zhang, Defect detection and life prediction of rolling element bearings, Thesis, Georgia Institue of Technology, Chapt. V, pp. 97–123, 2001 [Google Scholar]
  17. F. Bolaers, S. Rémond, X. Chiementin, S. Crequey, J.P. Dron, Modélisation de la force d’impact due à un écaillage de fatigue dans les roulements, Premier Colloque International IMPACT 2010, Djerba, Tunisie, 2010 [Google Scholar]
  18. J. Antoni, R.B. Randall, Differential diagnosis of gear and bearing faults, ASME J. Vibr. Acoust. 124 (2002) 165–171 [Google Scholar]
  19. J. Antoni, R.B. Randall, The spectral kurtosis: a useful tool for characterizing non-stationary signals. Mech. Syst. Signal Process. 20 (2006) 282–307 [Google Scholar]

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