EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 15 / No 6 (2014) Mechanics & Industry, 15 6 (2014) 467-476 References
Open Access
Issue
Mechanics & Industry
Volume 15, Number 6, 2014
Page(s) 467 - 476
DOI https://doi.org/10.1051/meca/2014061
Published online 16 September 2014
  1. A.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]
  2. S. Sassi, B. Badri, M. Thomas, Tracking surface degradation of ball bearings by means of new time domain scalar descriptors, Int. J. COMADEM 11 (2008) ISSN1363-7681 36–45 [Google Scholar]
  3. N. Tandon, A. Choudhury, 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]
  4. A. Choudhury, N. Tandon, Application of acoustic emission technique for the detection of defects in rolling element bearings, Tribol. Int. 33 (2000) 39–45 [CrossRef] [Google Scholar]
  5. X.Z. Yongyong He, I. Michael, Friswell, Defect diagnosis for rolling element bearings using acoustic emission, J. Vib. Acoust. 131 (2009) (ASME) [Google Scholar]
  6. A. Dadouche, et al., Sensitivity of Air-Coupled Ultrasound and Eddy Current Sensors to Bearing Fault Detection, Tribol. Trans. 51 (2008) 310–323 [CrossRef] [Google Scholar]
  7. J. Shiroishi et al., Bearing condition diagnosis via vibration and acoustic emission measurements, Mech. Syst. Signal Process. 11 (1997) 693–705 [CrossRef] [Google Scholar]
  8. Y.-H. Kim et al., Condition Monitoring of Low Speed Bearings: A Comparative Study of the Ultrasound Technique Versus Vibration Measurements, in Engineering Asset Management, Springer London, 2006, pp. 182–191 [Google Scholar]
  9. M. Kedadouche, M. Thomas, A. Tahan, Monitoring bearings by acoustic emission: a comparative study with vibration techniques for early detection, Proceedings of the 30th Seminar on machinery vibration, Canadian Machinery Vibration Association, Niagara Falls (ON, Canada), 2012, 17 p. [Google Scholar]
  10. X. Chiementin, D. Mba, B. Charnley, S. Lignon, J.-P. Dron, Effect of the denoising on acoustic emission signals, J. Vib. Acoust. 132 (2010) [Google Scholar]
  11. C. Liao, X. Li, D. Liu, Application of reassigned wavelet scalogram in feature extraction based on acoustic emission signal, J. Mech. Eng. 45 (2009) 273–279 [CrossRef] [Google Scholar]
  12. M. Zvokelj, S. Zupan, I. Prebil, Multivariate and multiscale monitoring of large-size low-speed bearings using ensemble empirical mode decomposition method combined with principal component analysis, Mech. Syst. Signal Process. (24) (2010) 1049–1067 [Google Scholar]
  13. B. Kilundu, et al., Cyclostationarity of Acoustic Emissions (AE) for monitoring bearing defects, Mech. Syst. Signal Process. 25 (2011) 2061–2072 [Google Scholar]
  14. J. Antoni, Cyclostationarity by examples, Mech. Syst. Signal Process. 23 (2009) 987–1036 [CrossRef] [Google Scholar]
  15. J. Antoni, F. Bonnardot, A. Raad, M. El Badaoui, Cyclostationary modelling of rotating machine vibration signals, Mech. Syst. Signal Process. (2004) 1285–1314 [Google Scholar]
  16. F. Bonnardot, R.B. Randall, F. Guillet, Extraction of 2nd order cyclostationary sources–application to vibration analysis, Mech. Syst. System Process. 19 (2005) 1230–1244 [CrossRef] [Google Scholar]
  17. R. Boustany, J. Antoni, A subspace method for the blind extraction of a cyclostationary source: application to rolling element bearing diagnostics, Mech. Syst. Syst. Process. 19 (2005) 1245–1259 [Google Scholar]
  18. R. Boustany, J. Antoni, Blind extraction of a cyclostationary signal using reduced-rank cyclic regression-A unifying approach, Mech. Syst. Syst. Process. (2008) 520–541 [Google Scholar]
  19. M. Thomas, J. Masounave, T.M. Dao, C.T. Le Dinh, F. Lafleur, Rolling element bearing degradation and vibration signature relationship, 2e Conférence Internationale sur les méthodes de surveillance et techniques de diagnostics acoustiques et vibratoires, SFM, Senlis, 1995, Vol. 1, pp. 267–277 [Google Scholar]
  20. J.I. Taylor, Identification of bearing defects by spectral analysis, J. Mech. Design 102 (1980) [Google Scholar]
  21. J. Antoni, Cyclic spectral analysis in practice, Mech. Syst. Signal Process. 21 (2007) 597–630 [CrossRef] [Google Scholar]
  22. J. Antoni, Cyclic spectral analysis of rolling element bearing signals: facts and fictions, J. Sound Vib. 304 (2007) 497–529 [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.