Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-16T09:09:55.399Z Has data issue: false hasContentIssue false
  • English
  • Français

Comparison between the efficiency of L.M.D and E.M.D algorithms for early detection of gear defects

Published online by Cambridge University Press:  19 June 2013

Kidar Thameur ,
Thomas Marc ,
Guilbault Raynald  and
El Badaoui Mohamed
Kidar Thameur
Affiliation:
Department of Mechanical Engineering,École de Technologie Supérieure, 1100 Notre-Dame Street West, Montreal, H3C 1K3, Quebec, Canada University of Lyon, University of Saint Etienne, LASPI, EA-3059, 20 Av de Paris, 42334 Roanne Cedex, France
Thomas Marc*
Affiliation:
Department of Mechanical Engineering,École de Technologie Supérieure, 1100 Notre-Dame Street West, Montreal, H3C 1K3, Quebec, Canada
Guilbault Raynald
Affiliation:
Department of Mechanical Engineering,École de Technologie Supérieure, 1100 Notre-Dame Street West, Montreal, H3C 1K3, Quebec, Canada
El Badaoui Mohamed
Affiliation:
University of Lyon, University of Saint Etienne, LASPI, EA-3059, 20 Av de Paris, 42334 Roanne Cedex, France
*
a Corresponding author: [email protected]
Get access

Abstract

In recent years, the adaptive decomposition methods have become the center of interest of researchers in many fields and especially in the vibration diagnosis of rotating machines. This paper compares the sensitivity of defect detection of two adaptive methods: local mean decomposition (L.M.D) and empirical mode decomposition (E.M.D). The efficiency of L.M.D and E.M.D methods for detecting defects is investigated for two cases, one from numerical signals and the other from signals recorded during a fatigue test on gear bench. The time descriptors Kurtosis, Thalaf and Thikat are applied on the signal and on its Hilbert transform. The results reveal that both techniques seem to be suitable and have good efficiency for the fault detection. From experimental signals, the comparative results reveal that both methods allow for monitoring wear, but that L.M.D is more sensitive to detect rapid changes of degradation than E.M.D method for the considered case. Consequently these features have potential to become powerful tools for the monitoring of rotating machinery.

Keywords

Gear defectlocal mean decompositionempirical mode decompositiondefect descriptorHilbert transform
Type
Research Article
Information
Mechanics & Industry , Volume 14 , Issue 2 , 2013 , pp. 121 - 127
Copyright
© AFM, EDP Sciences 2013

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Sassi, S., Badri, B., Thomas, M., 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
Badri, B., Thomas, M., Sassi, S., A shock filter for bearing slipping detection and multiple damage diagnosis, Int. J. Mech. 5 (2011) 318–326 Google Scholar
M. El Badaoui M, Contribution of vibratory diagnostic of gearbox by Cepstral analysis, Ph.D. thesis, Jean Monnet University of St Etienne (FR), 1999
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.
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.
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
Cheng, Junsheng, Yanga, Yi and Yanga, Yu, A rotating machinery fault diagnosis method based on local mean decomposition, Digit. Signal Process. 22 (2012) 356–366 CrossRefGoogle Scholar
Huang, N.E., Shen, Z., Long, S.R., 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 CrossRefGoogle Scholar
Huang, N.E., Shen, Z., Long, S.R., A New View of Nonlinear Water Waves: The Hilbert Spectrum, Annu. Rev. Fluid Mech. 31 (1999) 417–457 CrossRefGoogle Scholar
Yan, R.Q., Gao, R.X., Rotary Machine Health Diagnosis Based on Empirical Mode Decomposition, Trans. ASME, J. Vib. Acoust. 130 (2008) 021007 CrossRefGoogle Scholar
Du, Q., Yang, S., Improvement of the EMD Method and Applications in Defect Diagnosis of Ball Bearings, Meas. Sci. Technol. 17 (2006) 2355–2361 CrossRefGoogle Scholar
Gao, Q., Duan, C., Fan, H., Meng, Q., Rotating Machine Fault Diagnosis Using Empirical Mode decomposition, Mech. Syst. Signal Process. 22 (2008) 1072–1081 CrossRefGoogle Scholar
Smith, J.S., The Local Mean Decomposition and Its Application to EEG Perception Data, J. R. Soc. Interface 2 (2005) 443–454 CrossRefGoogle Scholar
Loutridis, S.J., Damage detection in gear systems using empirical mode decomposition, Eng. Struct. 26 (2004) 1833–1841 CrossRefGoogle Scholar