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Deep learning for predicting brake squeal
Publikationstyp
Conference Paper
Date Issued
2020
Sprache
English
Author(s)
Institut
TORE-URI
Start Page
3327
End Page
3337
Citation
International Conference on Noise and Vibration Engineering (ISMA 2020) and International Conference on Uncertainty in Structural Dynamics (USD 2020)
Scopus ID
Noise, vibration and harshness (NVH) development plays a crucial role in many mechanical engineering industries. For example, brake systems have been studied for decades to find instability mechanisms that enable the growth of friction-induced vibrations that are commonly known as brake squeal, moan, or others. Typically, brake system development is a data-heavy process involving lots of physical tests on components, larger assemblies and full vehicle test campaigns. In this contribution, we aim at leveraging the potential of data-driven methods that are currently transforming complete industries and scientific disciplines. Particularly, we illustrate how the brake sound detection task and the brake squeal prediction task can be approached using machine learning techniques. This contribution highlights some aspects of the in-depth study presented in [1].
Subjects
MLE@TUHH