Open Access
Mechanics & Industry
Volume 17, Number 3, 2016
Article Number 305
Number of page(s) 10
Published online 08 February 2016
  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. Instit. Eng. 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. P.D. 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. P.D. McFadden, J.D. Smith, Model for the vibration produced by a single point defect in a rolling element bearing, J. Sound Vib. 96 (1984) 69–82 [CrossRef] [Google Scholar]
  8. P.D. McFadden, J.D. Smith, The vibration produced by multiple point defects in a rolling element bearing, J. Sound Vib. 98 (1985) 263–273 [CrossRef] [Google Scholar]
  9. G. Dong, J. Chen, Noise resistant time frequency analysis and application in fault diagnosis of rolling element bearings, Mech. Syst. Signal Process. 33 (2012) 212–236 [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. 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]
  13. H. Wang, P. Chen, Fuzzy diagnosis method for rotating machinery in variable rotating Speed, IEEE Sensors Journal 11 (2011) 23–34 [CrossRef] [Google Scholar]
  14. J. McBain, M. Timusk, Fault detection in variable speed machinery: Statistical parameterization, J. Sound Vib. 237 (2009) 623–646 [CrossRef] [Google Scholar]
  15. K. Ait Sghir, F. Bolaers, O. Cousinard, J.P. Dron, Vibratory monitoring of a spalling bearing defect in variable speed regime, Mechanics & Industry 14 (2013) 129–136 [CrossRef] [EDP Sciences] [Google Scholar]
  16. L.F. Villa, A. Renones, J.R. Perana, J.L. De Miguel, Statistical fault diagnosis based on vibration analysis for gear test-bench under non-stationary conditions of speed and load, Mech. Syst. Signal Process. 29 (2012) 436–446 [CrossRef] [Google Scholar]
  17. N. Tandon, A. Choudury, An analytical model for the prediction of the vibration response of rolling element bearings due to localized defect, J. Sound Vib. 205 (1997) 275–292 [CrossRef] [Google Scholar]
  18. C. Zhang, Defect detection and life prediction of rolling element bearings, Thesis, Georgia Institute of Technology, 2001, Chap. V, pp. 97–123 [Google Scholar]
  19. F. Bolaers, S. Rémond, X. Chiementin, S. Crequey, J.P. Dron, Modélisation de la force d’impact due a un écaillage de fatigue dans les roulements, Premier Colloque International IMPACT 2010, Djerba, Tunisie, 2010, pp. 22–24 [Google Scholar]
  20. J. Antoni, R.B. Randall, Differential diagnosis of gear and bearing faults, ASME J. Vib. Acoust. 124 (2002) 165–171 [CrossRef] [Google Scholar]
  21. 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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