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 |
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