Methodological approach of selecting a vibration indicator in monitoring bearings

A rolling bearing is an important element in a rotating machine. Whatever the operating conditions, it is subject to fatigue which causes spalling. In aiming to obtain the most possible real fatigue curve, the vibration level is shown according to different statistical indicators such as the RMS (Root Mean Square), the kurtosis, the crest value, the crest factor and the peak ratio, then to choose the best of them that is able to show the evolution of the bearing degradation. In this work, through the experimental vibratory follow up of the thrust bearing spall using different statistical indicators, we present an optimization methodology in order to find a most significant indicator that is able to characterize the damage evolutio

Restitution of a temporal indicator corresponding to each vibratory source for a predictive maintenance.

L'analyse vibratoire vise à interpréter l'état de fonctionnement des machines tournantes et à réaliser le diagnostic des défauts vibratoires. En pratique, les signaux vibratoires enregistrés sont le résultat d'un mélange de différentes sources correspondant aux composants de la machine, ce qui rend di±cile l'interprétation de l'état d'endommagement d'un composant particulier. L'objectif de ce travail est de restituer les sources à partir du mélange obtenu, communément appelé problème inverse. Ces problèmes sont généralement instables et nécessitent des méthodes d'optimisation ou de stabilité. Ce papier propose de restituer la valeur RMS significative de chaque composant à partir de valeurs RMS globales résultant du mélange. Cette restitution est réalisée grâce à une méthode d'optimisation de la position des capteurs par analyse modale. Une validation sur un carter composé de deux roulements permet de démontrer l'efcacité de la méthode proposée

Following the growth of a rolling fatigue spalling for predictive maintenance = Suivi de la croissance d'un écaillage de fatigue de roulement d'une butée a billes dans le cadre d'une maintenance prédictive

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 failur