Current Methods for Operational Modal Analysis of Rotating Machinery and Prospects of Machine Learning

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/21060

Title:	Current Methods for Operational Modal Analysis of Rotating Machinery and Prospects of Machine Learning
Authors:	Sternharz, G Kalganova, T
Keywords:	Operational Modal Analysis;Rotating Machinery;Harmonic Excitation;Artificial Intelligence;Condition Monitoring
Issue Date:	24-Sep-2020
Publisher:	Springer
Citation:	Sternharz, 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.
URI:	https://bura.brunel.ac.uk/handle/2438/21060
DOI:	https://doi.org/10.1007/978-3-030-47721-9_19
ISBN:	978-3-030-47720-2 (hbk) 978-3-030-47721-9 (ebk)
Other Identifiers:	ORICD iD: Tatiana Kalganova https://orcid.org/0000-0003-4859-7152
Appears in Collections:	Dept of Electronic and Electrical Engineering Research Papers

Files in This Item:

File	Description	Size	Format
FullText.pdf	Copyright © 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).	339.66 kB	Adobe PDF	View/Open