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Communication Dans Un Congrès Année : 2017

Modelling vehicles NVH performance: a probabilistic approach

Gianluigi Brogna
Jérôme Antoni
Nicolas Totaro
Laurent Gagliardini
  • Fonction : Auteur
  • PersonId : 923232
Olivier Sauvage
  • Fonction : Auteur
  • PersonId : 939605

Résumé

The problem of the extensive exploitation of vehicle NVH measurements in real driving conditions is addressed. It is shown that through a probabilistic Bayesian approach a model of the noise level inside the cabin can be built from measurements. Two fully Bayesian algorithms are introduced: a Gaussian regression and a Scaled Mixture of Gaussians (SMOG) regression. Both are shown to be superior to a simple linear regression and the SMOG regression seems to be the most robust algorithm even in the presence of measurement noise. The model proposed can be used to get an estimation of the NVH performance of a vehicle, knowing only the operating conditions.
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Dates et versions

hal-01714112 , version 1 (21-02-2018)

Identifiants

  • HAL Id : hal-01714112 , version 1

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Gianluigi Brogna, Jérôme Antoni, Nicolas Totaro, Laurent Gagliardini, Olivier Sauvage. Modelling vehicles NVH performance: a probabilistic approach. Inter Noise 2017, Aug 2017, Hong Kong, China. ⟨hal-01714112⟩
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