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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: marc.thomas@etsmtl.ca
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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

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