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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/21060
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dc.contributor.authorSternharz, G-
dc.contributor.authorKalganova, T-
dc.contributor.editorDi Maio, D-
dc.contributor.editorBaqersad, J-
dc.coverage.spatialHouston, TX, USA-
dc.date.accessioned2020-06-23T10:09:03Z-
dc.date.available2020-06-23T10:09:03Z-
dc.date.issued2020-09-24-
dc.identifierORICD iD: Tatiana Kalganova https://orcid.org/0000-0003-4859-7152-
dc.identifier.citationSternharz, G. and Kalganova, T. (2020) 'Current methods for operational modal analysis of rotating machinery and prospects of machine learning', In: Di Maio, D. and Baqersad, J. (eds) Rotating Machinery, Optical Methods & Scanning LDV Methods, Volume 6. Conference Proceedings of the Society for Experimental Mechanics Series. Cham, Switzerland: Springer, pp. 155 - 163. doi: 10.1007/978-3-030-47721-9_19.-
dc.identifier.isbn978-3-030-47720-2 (hbk)-
dc.identifier.isbn978-3-030-47721-9 (ebk)-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/21060-
dc.description.sponsorshipEngineering and Physical Sciences Research Council (UK) and EXOLAUNCH GmbH (Germany).en_US
dc.format.extent155 - 163-
dc.format.mediumPrint-Electronic-
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.rightsCopyright © 2020 Springer Nature. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-47721-9_19 (see: https://www.springernature.com/gp/open-research/policies/book-policies).-
dc.rights.urihttps://www.springernature.com/gp/open-research/policies/book-policies-
dc.source38th IMAC, A Conference and Exposition on Structural Dynamics 2020-
dc.source38th IMAC, A Conference and Exposition on Structural Dynamics 2020-
dc.subjectOperational Modal Analysisen_US
dc.subjectRotating Machineryen_US
dc.subjectHarmonic Excitationen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectCondition Monitoringen_US
dc.titleCurrent Methods for Operational Modal Analysis of Rotating Machinery and Prospects of Machine Learningen_US
dc.typeConference Paperen_US
dc.identifier.doihttps://doi.org/10.1007/978-3-030-47721-9_19-
dc.relation.isPartOfRotating Machinery, Optical Methods & Scanning LDV Methods: Volume 6 Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics 2020-
pubs.publication-statusPublished-
dc.rights.holderSpringer Nature-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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