Probabilistic simulation for the certification of railway vehicles

The present dynamic certification process that is based on experiments has been essentially built on the basis of experience. The introduction of simulation techniques into this process would be of great interest. However, an accurate simulation of complex, nonlinear systems is a difficult task, in particular when rare events (for example, unstable behaviour) are considered. After analysing the system and the currently utilized procedure, this paper proposes a method to achieve, in some particular cases, a simulation-based certification. It focuses on the need for precise and representative excitations (running conditions) and on their variable nature. A probabilistic approach is therefore proposed and illustrated using an example. First, this paper presents a short description of the vehicle / track system and of the experimental procedure. The proposed simulation process is then described. The requirement to analyse a set of running conditions that is at least as large as the one tested experimentally is explained. In the third section, a sensitivity analysis to determine the most influential parameters of the system is reported. Finally, the proposed method is summarized and an application is presented

Characterization of the evolution of the train dynamic response under the effect of track irregularities

International audienceThere is a great interest to predict the long-time evolution of the track irregularities for a given track portion of the high-speed train network, in order to be able to anticipate the start off of the maintenance operations. In this paper, a stochastic predictive model is proposed for predicting the long-time evolution of a vector-valued random dynamic indicator related to the nonlinear dynamic responses of the high-speed train excited by the stochastic track irregularities. The long-time evolution of the vector-valued random indicator is modeled by a discrete non-Gaussian nonstationary stochastic model (ARMA type model), for which the coefficients are time-dependent. The quality assessment of the stochastic predictive model is presented, which validates the proposed stochastic model

Characterization of the evolution of the train dynamic response under the effect of track irregularities

International audienceThere is a great interest to predict the long-time evolution of the track irregularities for a given track portion of the high-speed train network, in order to be able to anticipate the start off of the maintenance operations. In this paper, a stochastic predictive model is proposed for predicting the long-time evolution of a vector-valued random dynamic indicator related to the nonlinear dynamic responses of the high-speed train excited by the stochastic track irregularities. The long-time evolution of the vector-valued random indicator is modeled by a discrete non-Gaussian nonstationary stochastic model (ARMA type model), for which the coefficients are time-dependent. The quality assessment of the stochastic predictive model is presented, which validates the proposed stochastic model

Predictive track maintenance: how statistics models and vehicle-track interaction open new prospects: Maintenance prédictive des voies : comment les modèles statistiques et l'interaction véhicule-voie ouvrent de nouvelles perspectives

National audienceLa thèse présentée dans cet article a été menée dans le cadre de recherches d'outils de maintenance prédictive de la voie. A partir du calcul de la réponse dynamique d'un train à grande vitesse sur la voie, d'un modèle statistique des défauts de géométrie d'une portion de voie et d'outils mathématiques, elle a pour objet de prévoir statistiquement l'évolution, à une échéance de temps donnée, de la réponse dynamique du train sur la portion de voie étudiée. Le modèle de prévision développé permet ainsi l'atteinte de valeurs seuils déclenchant une opération de maintenance de la voie

Predictive track maintenance: how statistics models and vehicle-track interaction open new prospects: Maintenance prédictive des voies : comment les modèles statistiques et l'interaction véhicule-voie ouvrent de nouvelles perspectives

National audienceLa thèse présentée dans cet article a été menée dans le cadre de recherches d'outils de maintenance prédictive de la voie. A partir du calcul de la réponse dynamique d'un train à grande vitesse sur la voie, d'un modèle statistique des défauts de géométrie d'une portion de voie et d'outils mathématiques, elle a pour objet de prévoir statistiquement l'évolution, à une échéance de temps donnée, de la réponse dynamique du train sur la portion de voie étudiée. Le modèle de prévision développé permet ainsi l'atteinte de valeurs seuils déclenchant une opération de maintenance de la voie

Stochastic prediction of high-speed train dynamics to long-term evolution of track irregularities

International audienceThere is a great interest to predict the long-term evolution of the track irregularities for a given track stretch of the high-speed train network, in order to be able to anticipate the start off of the maintenance operations. In this paper, a stochastic predictive model, based on big data made up of a lot of experimental measurements performed on the French high-speed train network, is proposed for predicting the statistical quantities of a vector-valued random indicator related to the nonlinear dynamic responses of the high-speed train excited by stochastic track irregularities. The long-term evolution of the vector-valued random indicator is modeled by a discrete non-Gaussian nonstationary stochas-tic model (ARMA type model), for which the coefficients are time-dependent. These coefficients are identified by a least-squares method and fitted on long time, using experimental measurements. The quality assessment of the stochas-tic predictive model is presented, which validates the proposed stochastic model

Sensitivity of train stochastic dynamics to long-time evolution of track irregularities

International audienceThe influence of the track geometry on the dynamic response of the train is of great concern for the railway companies, because they have to guarantee the safety of the train passengers in ensuring the stability of the train. In this paper, the long-term evolution of the dynamic response of the train on a stretch of the railway track is studied with respect to the long-term evolution of the track geometry. The characterization of the long-term evolution of the train response allows the railway companies to start off maintenance operations of the track at the best moment. The study is performed using measurements of the track geometry, which are carried out very regularly by a measuring train. A stochastic model of the studied stretch of track is created in order to take into account the measurement uncertainties in the track geometry. The dynamic response of the train is simulated with a multibody software. A noise is added in output of the simulation to consider the uncertainties in the computational model of the train dynamics. Indicators on the dynamic response of the train are defined, allowing to visualize the long-term evolution of the stability and the comfort of the train, when the track geometry deteriorates

Bayesian calibration of mechanical parameters of high-speed train suspensions

International audienceThe objective of the work presented here is a bayesian calibration of parameters describing the mechanical characteristics of high-speed train suspensions for maintenance purposes. This calibration is achieved by comparing simulation results to on-track accelerometric measurements. It requires the estimation on the multidimensionnal admissible set of the parameters of the likelihood function of the train dynamic response. This estimation is achieved thanks to the identification of a kriging metamodel of this likelihood function to reduce the numerical cost. From this metamodel, the posterior probability density function of the parameters is estimated using an MCMC algorithm