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Nowadays, geared systems are widely used in industrial applications such as automobiles, wind turbines, airplanes and other rotary machines. Gears generally undergo high service load, harsh operating conditions and inevitable fatigue; this is why maintainers consider them as a critical component. Therefore, early fault detection and diagnosis of gears leads to operational and maintenance cost reductions and ensure people safety; spur gear dynamic model can help to understand faults generation mechanism and to develop an effective faults detection and diagnosis methods. Models proposed in the literature can be gathered into three categories according to the method used for time varying mesh stiffness (TVMS) evaluation: analytical methods, finite element methods and experimental methods. Another classification of these models can be done following the number of parameters considered in the study like shafts torsional deformation and bearings stiffness and damping. In this study, the potential energy method is used to compute the TVMS of the gear pairs considering the hertzian component, bending component, shear component, axial compressive component and torsional component of the gear body, a 10 DOF model is developed to describe the dynamic behavior of a two stage gearbox including the bearing and the shafts stiffness. MATLAB Ode solvers are used for the resolution of the differential equation system to estimate the different quantities such as vibration and velocity in both healthy and faulty conditions.

International audience; In the last two decades, prediction of the remaining useful life (RUL) of critical components and more recently of systems (SRUL) received an increasing attention. Indeed, this prediction allows scheduling predictive maintenance actions in order to avoid damages caused by unexpected failures. A major part of the studies related to failure prognostics reported in the literature considers the mission profile as a static parameter when evaluating RUL/SRUL. However in practice, operating parameters can be adjusted according to the system health state for extending the SRUL while satisfying the performance requirements. Therefore, this paper addresses the issue of adjusting the mission profile of a multicompo-nent system in order to maximize its SRUL. To do this, a variant of the inoperability input-output model is used to represent the system degradation and take into account the interactions between its components. Then, the mission profile parameters (load levels and durations) are optimized using a genetic algorithm-based method to maximize the SRUL. The proposed method is finally applied to a mechatronic system to demonstrate its effectiveness.

In this paper, we propose an offline and online machine health assessment (MHA) methodology composed of feature extraction and selection, segmentation-based fault severity evaluation, and classification steps. In the offline phase, the best representative feature of degradation is selected by a new filter-based feature selection approach. The selected feature is further segmented by utilizing the bottom-up time series segmentation to discriminate machine health states, ie, degradation levels. Then, the health state fault severity is extracted by a proposed segment evaluation approach based on within segment rate-of-change (RoC) and coefficient of variation (CV) statistics. To train supervised classifiers, a priori knowledge about the availability of the labeled data set is needed. To overcome this limitation, the health state fault-severity information is used to label (eg, healthy, minor, medium, and severe) unlabeled raw condition monitoring (CM) data. In the online phase, the fault-severity classification is carried out by kernel-based support vector machine (SVM) classifier. Next to SVM, the k-nearest neighbor (KNN) is also used in comparative analysis on the fault severity classification problem. Supervised classifiers are trained in the offline phase and tested in the online phase. Unlike to traditional supervised approaches, this proposed method does not require any a priori knowledge about the availability of the labeled data set. The proposed methodology is validated on infield point machine sliding-chair degradation data to illustrate its effectiveness and applicability. The results show that the time series segmentation-based failure severity detection and SVM-based classification are promising.

International audience; This paper proposes a new adaptive prognostics approach consisting of hybrid feature selection and remaining-useful-life (RUL) estimation steps for railway point machines. In step-1, different time-domain based features are extracted and the best ones are selected by the hybrid feature selection method. Then, a degradation model is fitted to each of the selected features and the parameters are estimated. In step-2, the RUL of the component is predicted by using the proposed adaptive prognostics approach. The adaptive prognostics is based on the weighted likelihood combination of the estimated model parameters. The model parameters each of which estimated by curve fitting are used in the calculation of the likelihood probability weights. Then, an adaptive degradation model is built by using the weighted combination of the model parameter estimates and the component RUL is estimated. The proposed approach is validated on in-field point machine sliding-chair degradation and the results are discussed.

International audience; Feature extraction is one of the most important elements in Prognostics and Health Management (PHM) systems. Numerous techniques have been proposed for fault detection, diagnostics and prognostics in ball bearings which are key components of rotating machineries, widely used in the industry. Considering the strengths and weaknesses of these techniques, this paper aims at evaluating and analyzing different features in all three signal processing domains: time, frequency and time-frequency. The crucial indicators related to normal and abnormal cases are extracted from both vibration signals and stator current signals. Then, a new metric is proposed to measure the evolution of these indicators with respect to degradation levels of bearings. The performance of every indicator is analyzed to study which feature(s) is(are) better than other(s) and which feature(s) is(are) the best appropriate for vibration and current signals. These results could be effectively used in future for fault detection, diagnostics and prognostics applications.