Open Access
Issue
Mechanics & Industry
Volume 13, Number 1, 2012
Page(s) 33 - 44
DOI https://doi.org/10.1051/meca/2011150
Published online 23 April 2012
  1. B.D. Forrester, Use of Wigner Ville distribution in helicopter transmission fault detection in iProc of the Australian, symposium on signal Processing and Applications ASSPA89, Adelaide, Australia, 1989, pp. 77–82 [Google Scholar]
  2. D. Boulahbal, M.F. Golnaraghi, F. Ismail, Amplitude and phase wavelet maps for the detection of cracks in geared systems, Mech. Syst. Signal Process. 13 (1999) 423–436 [Google Scholar]
  3. C. Capdessus, M. Sidahmed, Cyclostationary processes application in gear faults early diagnosis, Mech. Syst. Signal Process. 14 (2000) 371–685 [Google Scholar]
  4. F. Bonnardot, M. El Badaoui, R.B. Randall, J. Danière, F. Guillet, Use of the acceleration signal of a gearbox in order to perform angular resampling (with limited speed fluctuation), Mech. Syst. Signal Process. 19 (2005) 766–785 [Google Scholar]
  5. Q. Gao, C. Duan, H. Fan, Q. Meng, Rotating machine fault diagnosis using empirical mode decomposition, Mech. Syst. Signal Process. 22 (2008) 1072–1081 [Google Scholar]
  6. F. Combet, L. Gelman, Optimal filtering of gear signals for early damage detection based on the spectral Kurtosis, Mech. Syst. Signal Process. 23 (2009) 652–668 [Google Scholar]
  7. N.E. Huang, Z. Shen, S.R. Long, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. Lond. Ser. 454 (1998) 903–995 [Google Scholar]
  8. 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 [Google Scholar]
  9. O. Andrade, S. Nasuto, P. Kyberd, C.M. Sweeney Reed, F.R.V. Kanijn, EMG signal filtering based on Empirical Mode Decomposition, Biomed. Signal Processing and Control 1 (2006) 44–55 [Google Scholar]
  10. Xu Guanlei, Wang Xiaotong, Xu Xiaogang, Improved bi-dimensional EMD and Hilbert spectrum for the analysis of textures, Pattern Recogn. 42 (2009) 718–734 [Google Scholar]
  11. Rong Jiang, Hong Yan, Studies of spectral properties of short genes using the wavelet subspace Hilbert-Huang transform (WSHHT), Physica A 387 (2008) 4223–4247 [Google Scholar]
  12. Xiaoli Li, Duan Li, Zhenhu Liang, Logan J. Voss, Jamie W. Sleigh, Analysis of depth of anesthesia with Hilbert-Huang spectral entropy, Clin. Neurophysiol. 119 (2008) 2465–2475 [PubMed] [Google Scholar]
  13. Jia-Rong Yeh, Shou-Zen Fan, Jiann-Shing Shieh, Human heart beat analysis using a modified algorithm of detrended fluctuation analysis based on empirical mode decomposition, Med. Eng. Phys. 31 (2009) 92–100 [PubMed] [Google Scholar]
  14. S.J. Loutridis, Damage detection in gear systems using empirical mode decomposition, Eng. Struct. 26 (2004) 1833–1841 [Google Scholar]
  15. B. Liu, S. Riemenschneider, Y. Xub, Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum, Mech. Syst. Signal Process. 17 (2005) 1–17 [Google Scholar]
  16. Z.K. Peng, P.W. Tse, F.L. Chu, A comparison study of improved Hilbert-Huang transform and wavelet transform : application to fault diagnosis for rolling bearing, Mech. Syst. Signal Process. 19 (2005) 974–988 [Google Scholar]
  17. A. Parey, M. El Badaoui, F. Guillet, N. Tandon, Dynamic modeling of spur gear pair and application of empirical mode decomposition-based statistical analysis for early detection of localized tooth defect, J. Sound Vib. 294 (2006) 547–561 [Google Scholar]
  18. H. Li, X. Deng, H. Dai, Structural damage detection using the combination method of EMD and wavelet analysis, Mech. Syst. Signal Process. 21 (2007) 298–306 [Google Scholar]
  19. Yimin Shao, Fengshou Gu, Fazenda. B. Ball A, Luyang Guan, Gearbox fault diagnosis under different operating conditions based on time synchronous average and ensemble empirical mode decomposition, ICCAS-SICE, 2009, pp. 383–388 [Google Scholar]
  20. Shufeng Ai, Hui Li, Gear Fault Detection Based on Ensemble Empirical Mode Decomposition and Hilbert-Huang Transform Fuzzy Systems and Knowledge Discovery, FSKD ’08, 2008, Vol. 3, pp. 173–177 [Google Scholar]
  21. P. Flandrin, G. Rilling, P. Goncalve, Empirical Mode Decomposition as a Filter Bank, IEEE Signal Process. Lett. 11 (2004) 112–114 [Google Scholar]
  22. N.E. Huang, M.L. Wu, S.R. Long, A confidence limit for the empirical mode decomposition and Hilbert spectral analysis, Proc. R. Soc. Lond. 459 (2003) 2317–2345 [Google Scholar]
  23. R.T. Rato, M.D. Ortigueira, A.G. Batista, On the HHT, its problems and some solutions, Mech. Syst. Signal Process. 22 (2008) 1374–1394 [Google Scholar]
  24. Yanli Yang, Jiahao Deng, Caipeng Wu, Analysis of mode mixing phenomenon in the empirical mode decomposition method, Second Int. Symp. Inf. Sci. Eng. IEEE (2009) 553–556 [Google Scholar]
  25. Zhaohua Wu, N.E. Huang, Ensemble empirical mode decomposition : a noise-assisted data analysis method, advances in adaptive data analysis 1, 2009, 1–41 c world scientific publishing company [Google Scholar]
  26. R.M. Stewart, Some useful data analysis techniques for gearbox diagnosis. Applications of time series analysis, Ph.D. Thesis, ISVR, University of Southampton, 1977 [Google Scholar]
  27. Siyan Wu, Ming J. Zuo, Anand Parey, Simulation of spur gear dynamics and estimation of fault growth, J. Sound Vib. 317 (2008) 608–624 [Google Scholar]
  28. P.D. Mcfadden, Detecting fatigue cracks in gear by amplitude and phase demodulation of the meshing vibration, ASME Journal of vibration, Acoustics, Stress, and Reliability in Design 108 (1986) 165–170 [Google Scholar]
  29. Enayet B. Halim, M.A.A. Shoukat Choudhury, Sirish L. Shah, Ming J. Zuo, Time domain averaging across all scales : A novel method for detection of gearbox faults, Mech. Sys. Signal Process. 22 (2008) 261–278 [Google Scholar]
  30. M. El Badaoui, F. Guillet, J. Daniere, New applications of the real cepstrum to gear signals, including definition of a robust fault indicator, Mech. Sys. Signal Process. 18, (2004) 1031–1046 [Google Scholar]
  31. L. Bouillaut, Approches cyclostationnaire et non linéaire pour l’analyse vibratoire de machines tournantes : Aspects théoriques et applications au diagnostic, Thèse Université de Technologie de Compiègne, 7 novembre 2000 [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.