Citation link: http://dx.doi.org/10.25819/ubsi/10122
DC FieldValueLanguage
dc.contributor.authorWiemann, Marcel-
dc.contributor.authorBonekemper, Lukas-
dc.contributor.authorKraemer, Peter-
dc.date.accessioned2022-07-12T12:02:33Z-
dc.date.available2022-07-12T12:02:33Z-
dc.date.issued2020de
dc.descriptionFinanziert aus dem DFG-geförderten Open-Access-Publikationsfonds der Universität Siegen für Zeitschriftenartikelde
dc.description.abstractThe vibration-based damage detection and the monitoring of modal data are currently based on different Operational Modal Analysis (OMA) approaches. For the continuous monitoring of modal quantities, different techniques for automated feature extraction are known. Especially in recent years several research groups and companies have been working on the automatic interpretation of stability plots. Nevertheless, many questions regarding data pre-processing for OMA in time or frequency domain are still unanswered. The present paper deals with issues regarding effective pre-processing methods for OMA based on Covariance-Stochastic Subspace Identification. In this context, the orthogonality of matrices after model order reduction, etc. are referred. This includes, for example, a comparison between the classical calculation of the reduced-order matrices and a procedure that preserves the orthogonality of these matrices. A method known from the signal denoising and image processing is also successful used to extract and select the modes. The mode extraction method is validated with an innovative three-dimensional stability plot. This paper does not claim to solve all tasks of an automated OMA, but it contributes the calculation of clean, easy to interpret, stability plots, which should facilitate the automatic evaluation in the future. The effectiveness of the algorithms is demonstrated by means of simulated (3DOF-StateSpace) and measured data of a laboratory structure described in [1]. Afterwards the results and the future works on the topic are discussed.en
dc.identifier.doihttp://dx.doi.org/10.25819/ubsi/10122-
dc.identifier.urihttps://dspace.ub.uni-siegen.de/handle/ubsi/2211-
dc.identifier.urnurn:nbn:de:hbz:467-22112-
dc.language.isoende
dc.rightsNamensnennung 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourceVibroengineering PROCEDIA, Vol. 31 (2020), S. 46-51. - https://doi.org/10.21595/vp.2020.21443de
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitende
dc.subject.otherAutomated operational modal analysisen
dc.subject.otherCovariance-driven stochastic subspace identificationen
dc.subject.otherStabilization diagramen
dc.subject.otherStructural health monitoringen
dc.subject.otherThree-dimensional stability plotsen
dc.subject.otherMode extractionen
dc.subject.otherAutomatisierte Betriebsmodalanalysede
dc.subject.otherKovarianzgetriebene stochastische Unterraumidentifikationde
dc.subject.otherStabilitätsdiagrammde
dc.subject.otherDreidimensionale Stabilitätsplotsde
dc.subject.otherModeextraktionde
dc.subject.swbModalanalysede
dc.titleMethods to enhance the automation of operational modal analysisen
dc.typeArticlede
item.fulltextWith Fulltext-
ubsi.publication.affiliationDepartment Maschinenbaude
ubsi.source.authorJVE Internationalde
ubsi.source.doi10.21595/vp.2020.21443-
ubsi.source.issn2538-8479-
ubsi.source.issued2020de
ubsi.source.issuenumber31de
ubsi.source.pagefrom46de
ubsi.source.pageto51de
ubsi.source.placeNeringade
ubsi.source.publisherJVE Internationalde
ubsi.source.titleVibroengineering PROCEDIAde
ubsi.subject.ghbsWBFde
ubsi.subject.ghbsWFBde
ubsi.subject.ghbsWCWde
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