High-speed train suspension health monitoring using computational dynamics and acceleration measurements

Abstract : This paper presents a novel method for the state health monitoring of high-speed train suspensions from in-line acceleration measurements by embedded sensors, for maintenance purposes. We propose a model-based method relying on a multibody simulation code. It performs the simultaneous identification of several suspension mechanical parameters. It is adapted to the introduction of uncertainties in the system and to the exploitation of numerous high-dimensional measurements. The novel method consists in a Bayesian calibration approach using a Gaussian process surro-gate model of the likelihood function. The method has been validated on numerical experiments. We demonstrate its ability to detect evolutions of the health state of suspension elements. It has then been tested on actual acceleration measurements to study the time evolution of the suspension parameters.
Complete list of metadatas

Cited literature [9 references]  Display  Hide  Download

https://hal-upec-upem.archives-ouvertes.fr/hal-02100228
Contributor : Christian Soize <>
Submitted on : Friday, April 26, 2019 - 2:28:10 PM
Last modification on : Thursday, July 18, 2019 - 4:36:07 PM

Identifiers

Collections

Citation

David Lebel, Christian Soize, Christine Fünfschilling, Guillaume Perrin. High-speed train suspension health monitoring using computational dynamics and acceleration measurements. Vehicle System Dynamics, Taylor & Francis, 2019, pp.1-22. ⟨10.1080/00423114.2019.1601744⟩. ⟨hal-02100228⟩

Share

Metrics

Record views

24