Stochastic prediction of high-speed train dynamics to long-term evolution of track irregularities. (July 2016)

• Record Type: Journal Article
• Authors: Lestoille, N.; Soize, C.; Funfschilling, C.
• Is Part Of: Mechanics research communications. Volume 75(2016:Jul.)
• Page Start: 29
• Page End: 39
• Publication Date: 2016-07
• Subjects: Railway track irregularities -- Train dynamics -- Stochastic prediction -- Nonstationary -- Non-Gaussian; Mechanics, Applied -- Periodicals; Mécanique appliquée -- Périodiques; Mechanics, Applied; Periodicals; 530
• DOI: 10.1016/j.mechrescom.2016.05.007
• Languages: English
• ISSNs: 0093-6413

Abstract

Abstract : Highlights: New stochastic model for long-time prediction of railway track irregularities. Nonlinear stochastic dynamics of high-speed trains with model uncertainties. Nonstationary and non-Gaussian ARMA model for the long-time-prediction model. Identification and validation of the stochastic model with experimental data. 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 stochastic 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 stochastic predictive model is presented, which validates the proposed stochastic model.