Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.2375
Publisher DOI: 10.3390/lubricants7080064
Title: Reconstruction of governing equations from vibration measurements for geometrically nonlinear systems
Language: English
Authors: Didonna, Marco 
Stender, Merten  
Papangelo, Antonio 
Fontanela, Filipe 
Ciavarella, Michele 
Hoffmann, Norbert  
Keywords: nonlinear dynamics;system identification;sparse regression;time series;geometric nonlinearity;contact
Issue Date: 5-Aug-2019
Publisher: Multidisciplinary Digital Publishing Institute
Source: Lubricants 7 (8): 64 (2019)
Journal or Series Name: Lubricants 
Abstract (english): Data-driven system identification procedures have recently enabled the reconstruction of governing differential equations from vibration signal recordings. In this contribution, the sparse identification of nonlinear dynamics is applied to structural dynamics of a geometrically nonlinear system. First, the methodology is validated against the forced Duffing oscillator to evaluate its robustness against noise and limited data. Then, differential equations governing the dynamics of two weakly coupled cantilever beams with base excitation are reconstructed from experimental data. Results indicate the appealing abilities of data-driven system identification: underlying equations are successfully reconstructed and (non-)linear dynamic terms are identified for two experimental setups which are comprised of a quasi-linear system and a system with impacts to replicate a piecewise hardening behavior, as commonly observed in contacts.
URI: http://hdl.handle.net/11420/3156
DOI: 10.15480/882.2375
ISSN: 2075-4442
Other Identifiers: doi: 10.3390/lubricants7080064
Institute: Strukturdynamik M-14 
Type: (wissenschaftlicher) Artikel
Funded by: Deutsche Forschungsgemeinschaft
Italian Ministry of Education, University and Research (MIUR)
Project: PA 3303/1-1 
Ho 3852/12-1 
Open Access Publizieren 2018 - 2019 / TU Hamburg 
grant no. L.232/2016 
Appears in Collections:Publications with fulltext (tub.dok)

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