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Following the growth of a rolling fatigue spalling for predictive maintenance

Published online by Cambridge University Press:  08 February 2013

Omar Djebili ,
Fabrice Bolaers ,
Ali Laggoun  and
Jean-Paul Dron
Omar Djebili*
Affiliation:
GRESPI, UFR, Sciences Exactes et Naturelles, Moulin de la Housse, BP 1039, 51687 Reims Cedex 2, France
Fabrice Bolaers
Affiliation:
GRESPI, UFR, Sciences Exactes et Naturelles, Moulin de la Housse, BP 1039, 51687 Reims Cedex 2, France
Ali Laggoun
Affiliation:
Departement de physique, Faculté des sciences UMBB, Boumerdes, Algerie
Jean-Paul Dron
Affiliation:
GRESPI, UFR, Sciences Exactes et Naturelles, Moulin de la Housse, BP 1039, 51687 Reims Cedex 2, France
*
a Corresponding author: omardjebili@yahoo.com
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Abstract

The bearing is one of the most important components of rotating machines. Nevertheless, in normal conditions of use, it is subject to fatigue which creates a defect called a rolling fatigue spalling. In this work, we present a follow-up of the thrust bearing fatigue on a test bench. Vibration analysis is a method used to characterize the defect. In order to obtain the fatigue curve more adjusted, we have studied the vibration level according to statistical indicators: the Root Mean Square value (RMS value), which is one of the best indicators to show the evolution of the bearing degradation. The approach follows the working of the bearing until the degradation with an on line acquisition of vibration statements in form of time signals. With the signal treatment, we obtain the values of the vibration amplitudes which characterize the vibration state of the bearing. Consequently, these values allow us to plot the fatigue curves. During our experimental work, this operation is applied for a batch of thrust bearings for which we have obtained similar fatigue curves where the evolution trend follows a regression model from the detection of the onset of the first spall. The result of this work will contribute to predict the working residual time before failure.

Keywords

Predictive maintenancebearingvibration analysisspallbearing fatigueMaintenance prédictiveroulementanalyse vibratoireécaillagefatigue de roulement
Type
Research Article
Information
Mechanics & Industry , Volume 14 , Issue 1 , 2013 , pp. 85 - 93
Copyright
© AFM, EDP Sciences 2013

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