Citation link: http://dx.doi.org/10.25819/ubsi/10123
DC FieldValueLanguage
dc.contributor.authorBonekemper, Lukas-
dc.contributor.authorWiemann, Marcel-
dc.contributor.authorKraemer, Peter-
dc.date.accessioned2022-06-03T11:55:41Z-
dc.date.available2022-06-03T11:55:41Z-
dc.date.issued2020de
dc.descriptionFinanziert aus dem DFG-geförderten Open-Access-Publikationsfonds der Universität Siegen für Zeitschriftenartikelde
dc.description.abstractTraditionally, modal analysis and the extraction of modal parameters from vibration data is a process that requires a more or less extensive amount of manual interaction from setting input parameters up until finding the eigenfrequencies. The growing interest in continuously monitoring mechanical structures e.g. for automated damage detection methods has led to the development of many approaches to automate different aspects of modal analysis. In this context, the Covariance-driven Stochastic subspace identification (Cov-SSI) is a widely used method. The present paper provides an automated Cov-SSI algorithm combined with a peak-picking approach for the automatic determination of input parameters. In this regard, using the Prominence-parameter allows to examine the PSD by finding the most relevant peaks. The herein shown algorithm is currently suitable for systems with a limited number of sensors. Cov-SSI results are arranged in stability plots and interpreted using the hierarchical clustering method. By creating stability plots for a wide range of block rows a sensitivity analysis is used to find the optimal result based on the averaged standard deviation of damping of the clusters in every stability plot. A second aspect of this paper is comparing the common method for order reduction with a modified method described in [1], which preserves the orthogonality of the , and matrix of the singular value decomposition. Exemplary results on both methods are provided using simulated data (state-space, 3 DoF)en
dc.identifier.doihttp://dx.doi.org/10.25819/ubsi/10123-
dc.identifier.urihttps://dspace.ub.uni-siegen.de/handle/ubsi/2212-
dc.identifier.urnurn:nbn:de:hbz:467-22121-
dc.language.isoende
dc.rightsNamensnennung 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourceVibroengineering PROCEDIA, Vol. 34 (2020), S. 43-49. - https://doi.org/10.21595/vp.2020.21742de
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitende
dc.subject.otherAutomated operational modal analysisen
dc.subject.otherCovariance-driven stochastic subspace identificationen
dc.subject.otherSensitivity analysisen
dc.subject.otherDetermining input parametersen
dc.subject.otherModified order reductionen
dc.subject.otherAutomatisierte operationelle Modalanalysede
dc.subject.otherKovarianzgesteuerte stochastische Unterraumidentifikationde
dc.subject.otherSensitivitätsanalysede
dc.subject.otherBestimmung der Eingangsparameterde
dc.subject.otherModifizierte Ordnungsreduktion-
dc.subject.swbModalanalysede
dc.subject.swbSensitivitätsanalysede
dc.titleAutomated set-up parameter estimation and result evaluation for SSI-Cov-OMAen
dc.typeArticlede
item.fulltextWith Fulltext-
ubsi.publication.affiliationDepartment Maschinenbaude
ubsi.source.authorJVE Internationalde
ubsi.source.doi10.21595/vp.2020.21742-
ubsi.source.issn2538-8479-
ubsi.source.issued2020de
ubsi.source.issuenumber34de
ubsi.source.pagefrom43de
ubsi.source.pageto49de
ubsi.source.placeNeringade
ubsi.source.publisherJVE Internationalde
ubsi.source.titleVibroengineering PROCEDIAde
ubsi.subject.ghbsWFBde
Appears in Collections:Publikationen aus der Universität Siegen
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