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- W3088397037 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." @default.
- W3088397037 created "2020-10-01" @default.
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- W3088397037 date "2020-01-01" @default.
- W3088397037 modified "2023-10-16" @default.
- W3088397037 title "Current Methods for Operational Modal Analysis of Rotating Machinery and Prospects of Machine Learning" @default.
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- W3088397037 doi "https://doi.org/10.1007/978-3-030-47721-9_19" @default.
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