Open Access
Mechanics & Industry
Volume 14, Number 2, 2013
Page(s) 121 - 127
Published online 19 June 2013
  1. S. Sassi, B. Badri, M. Thomas, Tracking surface degradation of ball bearings by means of new time domain scalar descriptors, Int. J. COMADEM, ISSN1363-7681 11 (2008) 36–45 [Google Scholar]
  2. B. Badri, M. Thomas, S. Sassi, A shock filter for bearing slipping detection and multiple damage diagnosis, Int. J. Mech. 5 (2011) 318–326 [Google Scholar]
  3. M. El Badaoui M, Contribution of vibratory diagnostic of gearbox by Cepstral analysis, Ph.D. thesis, Jean Monnet University of St Etienne (FR), 1999 [Google Scholar]
  4. D. Palaisi, R. Guilbault, M. Thomas, A. Lakis, N. Mureithi, Numerical simulation of vibratory behavior of damaged gearbox, (in French), Proceedings of the 27th Seminar on machinery vibration, Canadian Machinery Vibration Association, Vancouver, 2009, CB, 16p. [Google Scholar]
  5. M. Lamraoui, M. Thomas, M. El Badaoui, I. Zaghbani, V. Songméné, New Indicators Based on Cyclostationarity Approach for Machining Monitoring, Proceedings of Surveillance 6, Compiègne, 2011, paper 29. 27p. [Google Scholar]
  6. T. Kidar, M. Thomas, M. El Badaoui, R. Guilbault, Application of time descriptors to the modified Hilbert transform of empirical mode decomposition for early detection of gear defects, Proceedings of the 2nd conference on Condition Monitoring of Machinery in Non Stationnary Operations, 2012, Hammamet, Tunisia, pp. 471–480 [Google Scholar]
  7. Junsheng Cheng, Yi Yanga and Yu Yanga, A rotating machinery fault diagnosis method based on local mean decomposition, Digit. Signal Process. 22 (2012) 356–366 [CrossRef] [Google Scholar]
  8. N.E. Huang, Z. Shen, S.R. Long, The Empirical Mode Decomposition and Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis, Proc. R. Soc. London, Ser. A 454 (1998) 903–995 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  9. N.E. Huang, Z. Shen, S.R. Long, A New View of Nonlinear Water Waves: The Hilbert Spectrum, Annu. Rev. Fluid Mech. 31 (1999) 417–457 [NASA ADS] [CrossRef] [Google Scholar]
  10. R.Q. Yan, R.X. Gao, Rotary Machine Health Diagnosis Based on Empirical Mode Decomposition, Trans. ASME, J. Vib. Acoust. 130 (2008) 021007 [CrossRef] [Google Scholar]
  11. Q. Du, S. Yang, Improvement of the EMD Method and Applications in Defect Diagnosis of Ball Bearings, Meas. Sci. Technol. 17 (2006) 2355–2361 [CrossRef] [Google Scholar]
  12. Q. Gao, C. Duan, H. Fan, Q. Meng, Rotating Machine Fault Diagnosis Using Empirical Mode decomposition, Mech. Syst. Signal Process. 22 (2008) 1072–1081 [CrossRef] [Google Scholar]
  13. J.S. Smith, The Local Mean Decomposition and Its Application to EEG Perception Data, J. R. Soc. Interface 2 (2005) 443–454 [CrossRef] [PubMed] [Google Scholar]
  14. S.J. Loutridis, Damage detection in gear systems using empirical mode decomposition, Eng. Struct. 26 (2004) 1833–1841 [CrossRef] [Google Scholar]

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