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.