Open Access
Mechanics & Industry
Volume 16, Number 6, 2015
Article Number 610
Number of page(s) 15
Published online 04 September 2015
  1. P.D. Samuel, D.J. Pines, A review of vibration-based techniques for helicopter transmission diagnostics, J. Sound Vib. 282 (2005) 475–508 [Google Scholar]
  2. 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]
  3. M. Thomas, Fiabilité, maintenance prédictive et vibrations de machines, Presses de l’Université du Québec, Mai 2011, 633 p., D3357, ISBN 978-2-7605-3357-8 [Google Scholar]
  4. J.F. Kaiser, On a simple algorithm to calculate the energy of a signal, International Conference on Acoustics, Speech, and Signal Processing, 1990, pp. 381–384 [Google Scholar]
  5. A.A. Potamianos, P. Maragos, A comparison of the energy operator and the Hilbert transform approach to signal and speech demodulation, Signal Process. 37 (1994) 95–120 [CrossRef] [Google Scholar]
  6. P. Maragos, J.F. Kaiser, T.F. Quatieri, On amplitude and frequency demodulation using energy operators, IEEE Trans. Signal Process. 41 (1993) 1532–1550 [CrossRef] [Google Scholar]
  7. P. Maragos, J.F. Kaiser, T.F. Quatieri, Energy separation in signal modulations with application to speech analysis, IEEE Trans. Signal Process. 41 (1993) 3024–3051 [CrossRef] [Google Scholar]
  8. Cheng Junsheng, Yu Dejie, Yang Yu, The application of energy operator demodulation approach based on EMD in machinery fault diagnosis, Mech. Syst. Signal Process. 21 (2007) 668–677 [CrossRef] [Google Scholar]
  9. M. Kedadouche, M. Thomas, A. Tahan, Monitoring Machines by Using a Hybrid Method Combining MED, EMD, and TKEO, Adv. Acoust. Vib. 2014 (2014) 592080 [Google Scholar]
  10. Hui Li, Gear Fault detection based on angle domain Average and Teager Kaiser operator energy demodulation technique, Adv. Mater. Res. 204-210 (2011) 1411–1414 [CrossRef] [Google Scholar]
  11. Hui Li, Haiqi Zheng, Liwei Tang, Gear Fault Detection Based on Teager-Huang Transform, Int. J. Rotat. Mach. 2010 (2010) 502064 [Google Scholar]
  12. Hui. Li, EEMD and THT Based Gearbox Fault Detection and Diagnosis, Int. J. Digital Content Technol. Appl. 7 (2013) 229 [Google Scholar]
  13. Soltani Bozchalooi, Ming Liang1, Teager energy operator for multi-modulation extraction and its application for gearbox fault detection, Smart Mater. Struct. 19 (2010) 075008 [CrossRef] [Google Scholar]
  14. Xianyou Zhong, Liangcai Zeng, Chunhua Zhao, Xianming Liu, Shijun Chen, Fault Diagnosis for Wind Turbine Gearboxes Based on EMD and the Energy Operator, Appl. Mech. Mater. 28 (2013) 10–13 [CrossRef] [Google Scholar]
  15. Zhipeng Feng, Ming Liang, Yi Zhang, Shumin Hou, Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation, Renew. Energy 47 (2012) 112–126 [CrossRef] [Google Scholar]
  16. Y.G. Lei, J. Lin, Z.J. He, M.J. Zuo, A review on empirical mode decomposition in fault diagnosis of rotating machinery, Mech. Syst. Signal Process. 35 (2013) 108–126 [CrossRef] [Google Scholar]
  17. M. Kedadouche, M. Thomas, A. Tahan, Empirical Mode Decomposition of Acoustic Emission for Early Detection of Bearing Defects, 3rd International Conference on Condition Monitoring of Machinery in Non-Stationary Operations Ferrara, Italy, 2013, p. 9 [Google Scholar]
  18. N. Sawalhi, R.B. Randall, H. Endo, The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis, Mech. Syst. Signal Process. 21 (2007) 2616–2633 [CrossRef] [Google Scholar]
  19. H. Endo, R.B. Randall, Application of a minimum entropy deconvolution filter to enhance autoregressive model based gear tooth fault detection technique, Mech. Syst. Signal Process. 21 (2007) 906–919 [CrossRef] [Google Scholar]
  20. Z.H. Wu, N.E. Huang, Ensemble empirical mode decomposition: a noise assisted data analysis method, Adv. Adapt. Data Anal. 1 (2009) 11–41 [Google Scholar]
  21. Y. Lei, Z. He, Y. Zi, Application of the EEMD method to rotor fault diagnosis of rotating machinery, Mech. Syst. Signal Process. 23 (2009) 1327–1338 [CrossRef] [Google Scholar]
  22. Y. Lei, Z. He, Y. Zi, EEMD method and WNN for fault diagnosis of locomotive roller bearings, Expert Syst. Appl. 38 (2011) 7334–7341 [CrossRef] [Google Scholar]
  23. Y. Lei, M.J. Zuo, Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs, Meas. Sci. Technol. 20 (2009) 125701 [CrossRef] [Google Scholar]
  24. Zhou, T. Tao, X. Mei, G. Jiang, N. Sun, Feed-axis gearbox condition monitoring using built-in position sensors and EEMD method, Robotics and Computer-Integrated Manufacturing 27 (2011) 785–793 [CrossRef] [Google Scholar]
  25. R.A. Wiggins, Minimum entropy deconvolution, Geoexploration 16 (1978) 21–35 [Google Scholar]
  26. G.L. McDonald, Qing Zhao, Ming J. Zuo, Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection, Mech. Syst. Signal Process. 21 (2007) 2616–2633 [CrossRef] [Google Scholar]
  27. P.D. Mc Fadden, Detecting fatigue cracks in gears by amplitude and phase demodulation of the meshing vibration, J. Vib. Acoust. Stress Reliab. Design 108 (1986) 165–170 [Google Scholar]
  28. A. Pareya, M. El Badaoui, F. Guillet, N. Tandon, Dynamic modelling 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 [CrossRef] [Google Scholar]
  29. W. Wang, Early detection of gear tooth cracking using the resonance demodulation technique, Mech. Syst. Signal Process. 5 (2001) 887–903. [CrossRef] [Google Scholar]
  30. R.B. Randall, A new method of modelling gear faults, J. Mech. Design 104 (1982) 259–267 [CrossRef] [Google Scholar]
  31. M. El Badaoui, F. Guillet, J. Daniere, New applications of the real cepstrum to gear signals, including definition of a robust fault indicator, Mech. Syst. Signal Process. 18 (2004) 103–1046 [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.