Inverse identification of mechanical parameters of high-speed train suspensions submitted to random track irregularities

International audienceThe objective of the work presented here is the inverse identification of parameters describing the mechanical characteristics of high-speed train suspensions for maintenance purposes. This identification is achieved by comparing simulation results to on-track accelerometric measurements. It requires the introduction of an output predictive error as a noise added to simulation outputs to take into account measure and model uncertainties, the introduction of an objective function defined on the parameter domain, and the optimization of this objective function, performed by an EGO (Efficient Global Optimization) algorithm thanks to the construction of a Kriging metamodel of the objective function