Bayesian calibration of high-speed train suspensions parameters using Kriging metamodeling

David Lebel 1, 2 Christian Soize 2 Christine Funfschilling 1 Guillaume Perrin 3
1 SNCF : Innovation & Recherche
2 MSME - Laboratoire de Modélisation et Simulation Multi Echelle
3 DAM/DIF - DAM Île-de-France
Abstract : 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.
Keywords : statistical inverse problem train dynamics state health monitoring Bayesian calibration Kriging
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Conference papers
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https://hal-upec-upem.archives-ouvertes.fr/hal-01810455
Contributor : Christian Soize <>
Submitted on : Thursday, June 7, 2018 - 9:50:35 PM
Last modification on : Friday, October 4, 2019 - 1:32:32 AM

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David Lebel, Christian Soize, Christine Funfschilling, Guillaume Perrin. Bayesian calibration of high-speed train suspensions parameters using Kriging metamodeling. IMAC XXXVIII, Feb 2018, Orlando, Florida, United States. pp.1-3. ⟨hal-01810455⟩

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