Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.1991
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DC FieldValueLanguage
dc.contributor.authorStender, Merten-
dc.contributor.authorOberst, Sebastian-
dc.contributor.authorHoffmann, Norbert-
dc.date.accessioned2019-01-24T12:42:48Z-
dc.date.available2019-01-24T12:42:48Z-
dc.date.issued2019-01-10-
dc.identifier.citationVibration 2 (1): 25-46 (2019)de_DE
dc.identifier.issn2571-631Xde_DE
dc.identifier.urihttps://hdl.handle.net/11420/1994-
dc.description.abstractTime recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage of classical spectral signal processing techniques for (a) describing the dynamics and (b) deriving discriminative features from the data. However, common model identification and validation techniques mostly rely on steady-state recordings, characteristic spectral properties and non-transient behavior. In this work, a recent method, which allows reconstructing differential equations from time series data, is extended for higher degrees of automation. With special focus on short and strongly damped oscillations, an optimization procedure is proposed that fine-tunes the reconstructed dynamical models with respect to model simplicity and error reduction. This framework is analyzed with particular focus on the amount of information available to the reconstruction, noise contamination and nonlinearities contained in the time series input. Using the example of a mechanical oscillator, we illustrate how the optimized reconstruction method can be used to identify a suitable model and how to extract features from uni-variate and multivariate time series recordings in an engineering-compliant environment. Moreover, the determined minimal models allow for identifying the qualitative nature of the underlying dynamical systems as well as testing for the degree and strength of nonlinearity. The reconstructed differential equations would then be potentially available for classical numerical studies, such as bifurcation analysis. These results represent a physically interpretable enhancement of data-driven modeling approaches in structural dynamics.en
dc.language.isoende_DE
dc.publisherMultidisciplinary Digital Publishing Institutede_DE
dc.relation.ispartofVibrationde_DE
dc.rightsCC BY 4.0de_DE
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.subjectsignal processingde_DE
dc.subjectsparse regressionde_DE
dc.subjectsystem identificationde_DE
dc.subjectimpulse responsede_DE
dc.subjectoptimizationde_DE
dc.subjectfeature generationde_DE
dc.subjectstructural dynamicsde_DE
dc.subjecttime series classificationde_DE
dc.subject.ddc500: Naturwissenschaftende_DE
dc.subject.ddc530: Physikde_DE
dc.titleRecovery of differential equations from impulse response time series data for model identification and feature extractionde_DE
dc.typeArticlede_DE
dc.date.updated2019-01-24T09:21:47Z-
dc.identifier.urnurn:nbn:de:gbv:830-882.026011-
dc.identifier.doi10.15480/882.1991-
dc.type.diniarticle-
dc.subject.ddccode530-
dc.subject.ddccode500-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.026011-
tuhh.oai.showtruede_DE
dc.identifier.hdl11420/1994-
tuhh.abstract.englishTime recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage of classical spectral signal processing techniques for (a) describing the dynamics and (b) deriving discriminative features from the data. However, common model identification and validation techniques mostly rely on steady-state recordings, characteristic spectral properties and non-transient behavior. In this work, a recent method, which allows reconstructing differential equations from time series data, is extended for higher degrees of automation. With special focus on short and strongly damped oscillations, an optimization procedure is proposed that fine-tunes the reconstructed dynamical models with respect to model simplicity and error reduction. This framework is analyzed with particular focus on the amount of information available to the reconstruction, noise contamination and nonlinearities contained in the time series input. Using the example of a mechanical oscillator, we illustrate how the optimized reconstruction method can be used to identify a suitable model and how to extract features from uni-variate and multivariate time series recordings in an engineering-compliant environment. Moreover, the determined minimal models allow for identifying the qualitative nature of the underlying dynamical systems as well as testing for the degree and strength of nonlinearity. The reconstructed differential equations would then be potentially available for classical numerical studies, such as bifurcation analysis. These results represent a physically interpretable enhancement of data-driven modeling approaches in structural dynamics.de_DE
tuhh.publisher.doi10.3390/vibration2010002-
tuhh.publication.instituteStrukturdynamik M-14de_DE
tuhh.identifier.doi10.15480/882.1991-
tuhh.type.opus(wissenschaftlicher) Artikel-
tuhh.institute.germanStrukturdynamik M-14de
tuhh.institute.englishStrukturdynamik M-14de_DE
tuhh.gvk.hasppnfalse-
openaire.rightsinfo:eu-repo/semantics/openAccessde_DE
dc.type.driverarticle-
dc.rights.ccversion4.0de_DE
dc.type.casraiJournal Article-
tuhh.container.issue1de_DE
tuhh.container.volume2de_DE
tuhh.container.startpage25de_DE
tuhh.container.endpage46de_DE
dc.rights.nationallicensefalsede_DE
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.creatorGNDStender, Merten-
item.creatorGNDOberst, Sebastian-
item.creatorGNDHoffmann, Norbert-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.creatorOrcidStender, Merten-
item.creatorOrcidOberst, Sebastian-
item.creatorOrcidHoffmann, Norbert-
item.openairetypeArticle-
item.grantfulltextopen-
crisitem.author.deptStrukturdynamik M-14-
crisitem.author.deptStrukturdynamik M-14-
crisitem.author.deptStrukturdynamik M-14-
crisitem.author.orcid0000-0002-0888-8206-
crisitem.author.orcid0000-0002-1388-2749-
crisitem.author.orcid0000-0003-2074-3170-
crisitem.author.parentorgStudiendekanat Maschinenbau-
crisitem.author.parentorgStudiendekanat Maschinenbau-
crisitem.author.parentorgStudiendekanat Maschinenbau-
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