EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 14 / No 1 (2013) Mechanics & Industry, 14 1 (2013) 85-93 References
Open Access
Issue
Mechanics & Industry
Volume 14, Number 1, 2013
Page(s) 85 - 93
DOI https://doi.org/10.1051/meca/2012042
Published online 08 February 2013
  1. R.J. Alfredson, J. Mathew, Frequency domain methods for monitoring the condition of rolling element bearings, Mech. Eng. Trans. Inst. Eng. Aust. 2 (1985) 102–107 [Google Scholar]
  2. R.J. Alfredson, J. Mathew, Time domain methods for monitoring the condition of rolling element bearings, Mech. Eng. Trans, Inst. Eng. Aust. 2 (1985) 108–112 [Google Scholar]
  3. P.F.J. Burgess, Antifriction bearing fault detection using envelope detection, Trans, Inst. Prof., New Zealand-Elect. Mech. Chem. Eng. Sec. 15 (1998) 77–82 [Google Scholar]
  4. Y. Li, S. Billington, C. Zhang, Dynamic prognostic prediction of defect propagation on rolling element bearing, Lubrification Eng. 42 (1999) 385–392 [Google Scholar]
  5. A. Palmgren, Ball and Roller Bearing Engineering, Philadelphia, PA : Burbank, 1959 [Google Scholar]
  6. D. Nelias, Contribution à l’étude des roulements, Modélisation globale des roulements et avaries superficielles dans les contacts EHD pour des surfaces réelles ou indentées, Habilitation à diriger les recherches, Lyon : INSA de Lyon, 1999, 160 p. [Google Scholar]
  7. W. Cheng, H.S. Cheng, T. Mura, L.M. Keer, Micro mechanics modeling of crack initiation under contact fatigue, ASME J. Tribol. 116 (1994) 2–8 [Google Scholar]
  8. D. Nelias, M.L. Dumont, F. Couhier, G. Dudragne, L. Flamand, Experimental and theorical investigation on rolling contact fatigue of 52100 and M50 steels under EHL or micro-EHL condition, ASME J. Tribol. 120 (1998) 184–190 [CrossRef] [Google Scholar]
  9. Y. Li, S. Billigton, C. Zhang, T. Kurfess, S. Danyluk, S. Liang, Adaptive pronostics for rolling element bearing condition, Mech. Syst. Signal Proc. 13 (1999) 103–113 [CrossRef] [Google Scholar]
  10. Y. Li, T.R. Kurfess, S.Y. Liang, Stochastic prognostics for rolling element bearings, Mech. Syst. Signal Proc. 14 (2000) 747–762 [CrossRef] [Google Scholar]
  11. J. Shiroishi, Y. Li, T. Kurfess, S. Danyluk, Bearing condition diagnostics via vibration and acoustic emission measurements, Mech. Syst. Signal Proc. 11 (1997) 693–705 [Google Scholar]
  12. J. Qiu, B.B. Set, S.Y. Liang, C. Zhang, Damage mechanics approach for bearing lifetime prognostics, Mech. Syst. Signal Proc. 16 (2002) 817–829 [CrossRef] [Google Scholar]
  13. T.I. Liu, J.H. Singonhalli, Detection of roller bearing defects using expert system and fuzzy logic, Mech. Syst. Signal Proc. 10 (1996) 595–614 [Google Scholar]
  14. I.E. Alguindigue, A. Loskiewicz-Buczak, R.E. Ubrig, Monitoring and diagnosis of rolling element bearings using artificial neural networks, IEEE Trans. Ind. Electron 40 (1993) 209–217 [CrossRef] [Google Scholar]
  15. Y. Shao, K. Nezu, Prognosis of remaining bearing life using neural networks, Proc. Inst. Mech. Eng., J. Syst. Control. Eng. 214 (2000) 217–230 [Google Scholar]
  16. R. Huanga, L. Xia, X. Lib, C. Liuc, H. Qiud, J.L. Richard, Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods, Mech. Syst. Signal Proc. 21 (2007) 193–207 [CrossRef] [Google Scholar]
  17. A. Ray, S. Tangirala, Stochastic modeling of fatigue crack dynamics for on-line failure prognostics, IEEE Trans. Control Systems Technol. 4 (1996) 4 [Google Scholar]
  18. P.D. McFadden, J.D. Smith, Vibration monitoring of rolling element bearings by the high frequency resonance technique, Rev. Tribol. Int. 17 (1984) 3-10 [Google Scholar]
  19. R.B. Randall, J. Antoni, Rolling element bearing diagnostics, Mech. Sys. Signal Proc. (2011) 485–520 [Google Scholar]
  20. T. Williams, X. Ribadeneira, Rolling element bearing diagnostics in run-to-failure lifetime testing, Mech. Syst. Signal Proc. 15 (2001) 979–993 [Google Scholar]
  21. H. Qiu, J. Lee, J. Lin, Y. Gang, Robust performance degradation assessment methods for enhanced rolling element bearing prognostics, Advanced Engineering Informatics 17 (2003) 127–140 [Google Scholar]
  22. N. Gebraeel, M. Lawley, Residual life predictions from vibration-based degradation signals : A neural network approach, IEEE Trans. Industrial Elect. 51 (2004) [Google Scholar]
  23. L. Rosado, N.H. Forster, K.L. Thomson, J.W. Cooke, Rolling contact fatigue life and spall propagation of AISI M50, M50NiL, and AISI 52100, Part I : Experimental Results, Tribol. Trans. 53 (2010) 29–41 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.