Publications Internationales
Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13
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Item Deep convolutional neural networks for Bearings failure predictionand temperature correlation(JVE International, 2018) Belmiloud, D.; Benkedjouh, T.; Lachi, Mohammed; Laggoun, A.; Dron, J. P.Rolling elements bearings (REBs) is one of the most sensitive components and the common failure unit in mechanical equipment. Bearings failure prognostics, which aims to achieve an effective way to handle the increasing requirements for higher reliability and in the same time reduce unnecessary costs, has been an area of extensive research. The accurate prediction of bearings Remaining Useful Life (RUL) is indispensable for safe and lifetime-optimized operations. To monitor this vital component and planning repair work, a new intelligent method based on Wavelet Packet Decomposition (WPD) and deep learning networks is proposed in this paper. Firstly, features extraction from WPD used as input data. Secondly, these selected features are fed into deep Convolutional Neural Networks (CNNs) to construct the Health Indicator (HI). This study focuses on analysing the relationships such as correlations between the HI and temperature. We develop a solution for the Connectiomics contest dataset of bearings under different operating conditions and severity of defects. The performance of the proposed method is verified by four bearing data sets collected from experimental setup called “PRONOSTIA”. The results show that the health indicator obtains fairly high monotonicity and correlation values and it is beneficial to bearing life prediction. In addition, it is experimentally demonstrated that the proposed method is able to achieve better performance than a traditional neural network based methodItem Methodological approach of selecting a vibration indicator in monitoring bearings(Academic Journals, 2013) Djebili, Omar; Bolaers, F.; Laggoun, A.; Dron, J. P.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 evolutionItem 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(Cambridge University Press, 2013) Djebili, Omar; Bolaers, F.; Laggoun, A.; Paul Dron, J.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