Machine learning and system identification for the estimation of data-driven models: an experimental case study illustrated on a tire-suspension system

Mahmoud El-kafafy, Péter Zoltán Csurcsia, Bram Cornelis, Risaliti Enrico, Karl Janssens

Research output: Chapter in Book/Report/Conference proceedingConference paper

Abstract

In this paper, we are investigating the capabilities of both classical system identification and modern machine learning (time regression neural networks) to derive predictive black-box models which can predict the wheel center loads (WCLs) by making use of either the road disturbances acting on the wheel-patch or strain measurements on the suspension components. The WCLs are very important for the durability analyses of a vehicle and their direct measurement requires the use of expensive force transducers. Thus, predictive black-box models can augment data sets by predictions of the WCLs when multiple vehicles need to be tested and where it is infeasible to instrument dedicated transducers in each vehicle. As identification techniques to be used to derive those black-box models, the linear ARX model, the polynomial nonlinear state space (PNLSS) method, and the time regression neural networks (NNs) will be reviewed and benchmarked using experimental data measured on a McPherson suspension test rig.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Noise and Vibration Engineering (ISMA)
EditorsW. Desmet, B. Pluymers, D. Moens, S. Vandemaele
PublisherKU Leuven
Pages3287-3301
Number of pages15
ISBN (Electronic)9789082893113
ISBN (Print)9789082893113
Publication statusPublished - 9 Sep 2020
EventInternational Conference on Noise and Vibration Engineering 2020 - Leuven, Belgium
Duration: 7 Sep 20209 Sep 2020

Publication series

NameProceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics

Conference

ConferenceInternational Conference on Noise and Vibration Engineering 2020
Abbreviated titleISMA2020
CountryBelgium
CityLeuven
Period7/09/209/09/20

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