Data-driven reduced order modeling for mechanical oscillators using Koopman approaches

Data-driven reduced order modeling methods that aim at extracting physically meaningful governing equations directly from measurement data are facing a growing interest in recent years. The HAVOK-algorithm is a Koopman-based method that distills a forced, low-dimensional state-space model for a given dynamical system from a univariate measurement time series. This article studies the potential of HAVOK for application to mechanical oscillators by investigating which information of the underlying system can be extracted from the state-space model generated by HAVOK. Extensive parameter studies are performed to point out the strengths and pitfalls of the algorithm and ultimately yield recommendations for choosing tuning parameters. The application of the algorithm to real-world friction brake system measurements concludes this study.

Subjects

structural dynamics

system identification

state-space models

state-space embeddings

sparse measurement data

modal analysis

DDC Class

530: Physik

Funding(s)

SPP 1897: Calm, Smooth and Smart - Novel Approaches for Influencing Vibrations by Means of Deliberately Introduced Dissipation: Teilprojekt Simulationsbasierter Entwurf hybrider Partikeldämpfer mit Anwendung auf flexible Mehrkörpersysteme

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by/4.0/

Name

fams-09-1124602.pdf

Size

3.92 MB

Format

Adobe PDF

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Data-driven reduced order modeling for mechanical oscillators using Koopman approaches