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

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Rotating Machinery, Optical Methods & Scanning LDV Methods, Volume 6

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Abstract

Current demands for operational efficiency and safety of aircraft engines require a deep understanding of engine component behavior. Mechanical spinning tests can be utilized for this purpose by extracting modal properties of the tested hardware by Operational Modal Analysis. However, this process is challenging because the operational input force is unknown and is disturbed by influences such as harmonics in the excitation, which are characteristic for rotating hardware. To address this issue, this paper provides a qualitative review of existing methods for Operational Modal Analysis (OMA) applied to rotating machinery. A plate structure under random excitation with additional harmonic loading, which resembles the effect of rotation, is presented. The vibration response of the plate has been processed by six different OMA methods to estimate their applicability and output variance. The results of the conducted research and experimental analysis indicate that a hybrid approach based on machine learning and different vibration analysis methods can be beneficial. Specifically, data fusion and condition monitoring can be facilitated to acquire more consistent and accurate analysis results and ultimately contribute to optimized engine system designs.

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Acknowledgements

We would like to express our gratitude to Cristinel Mares from Brunel University London as well as Moritz Meyeringh from Rolls-Royce Deutschland for their valuable consultations. This work was also supported by the Engineering and Physical Sciences Research Council (UK) and EXOLAUNCH GmbH (Germany).

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Authors and Affiliations

  1. Department of Electronic and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, Middlesex, UK

    German Sternharz & Tatiana Kalganova

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  1. German Sternharz
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  2. Tatiana Kalganova
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Correspondence to German Sternharz .

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Editors and Affiliations

  1. University of Twente, Enschede, Twente, The Netherlands

    Dario Di Maio

  2. Kettering University, Flint, MI, USA

    Javad Baqersad

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Sternharz, G., Kalganova, T. (2020). Current Methods for Operational Modal Analysis of Rotating Machinery and Prospects of Machine Learning. In: Di Maio, D., Baqersad, J. (eds) Rotating Machinery, Optical Methods & Scanning LDV Methods, Volume 6. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-030-47721-9_19

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