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

Abstract : There 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.
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Contributor : Christian Soize Connect in order to contact the contributor
Submitted on : Thursday, June 2, 2016 - 9:23:25 AM
Last modification on : Thursday, September 29, 2022 - 2:21:15 PM
Long-term archiving on: : Saturday, September 3, 2016 - 10:40:28 AM


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Nicolas Lestoille, Christian Soize, Christine Funfschilling. Stochastic prediction of high-speed train dynamics to long-term evolution of track irregularities. Mechanics Research Communications, 2016, 75, pp.29-39. ⟨10.1016/j.mechrescom.2016.05.007⟩. ⟨hal-01325285⟩



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