Didonna, MarcoMarcoDidonnaStender, MertenMertenStenderPapangelo, AntonioAntonioPapangeloFontanela, FilipeFilipeFontanelaCiavarella, MicheleMicheleCiavarellaHoffmann, NorbertNorbertHoffmann2019-08-212019-08-212019-08-05Lubricants 7 (8): 64 (2019)http://hdl.handle.net/11420/3156Data-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.en2075-444220198Multidisciplinary Digital Publishing Institutehttps://creativecommons.org/licenses/by/4.0/nonlinear dynamicssystem identificationsparse regressiontime seriesgeometric nonlinearitycontactIngenieurwissenschaftenJournal Article2019-08-09urn:nbn:de:gbv:830-882.04525810.15480/882.237510.3390/lubricants708006410.15480/882.2375Other