Bayesian calibration of high-speed train suspensions parameters using Kriging metamodeling
Résumé
The objective of the work presented here is to perform the Bayesian calibration of parameters describing the mechanical properties of high-speed train suspensions for maintenance purposes. This calibration requires joint measurements of the track geometric irregularities and of the train dynamic response. The calculation of the likelihood function relies on simulation, which makes it computationally expensive. Therefore, the likelihood function is represented by a Kriging metamodel. We present a calibration method that allows for taking into account the uncertainty introduced by the use of this metamodel.