One of the major applications of Structural Dynamics in Civil, Mechanical, or Aerospace Engineering regards the dynamic characterisation of man-made structures and components. Yet, traditional Experimental Modal Analysis (EMA) needs dedicated setups which may not be always available where and when needed. For these and other reasons, output-only Operational Modal Analysis (OMA) is regarded as a more practical and convenient alternative. Many OMA algorithms have been reported in the scientific literature during the last twenty and more years. In this study, an Automatic OMA method is presented. The proposed algorithm is completely independent of the user experience, fully objective, and based on statistical principles and a Machine Learning (ML) clustering approach. The AOMA code is firstly applied to a numerical case study, to test all the parameters which control the process. An Airbus H135 helicopter blade is then analysed to verify the performance of the algorithm experimentally.

A machine learning approach for automatic operational modal analysis / Mugnaini, V.; Zanotti Fragonara, L.; Civera, M.. - In: MECHANICAL SYSTEMS AND SIGNAL PROCESSING. - ISSN 0888-3270. - 170:(2022), p. 108813. [10.1016/j.ymssp.2022.108813]

A machine learning approach for automatic operational modal analysis

Mugnaini V.;Zanotti Fragonara L.;Civera M.
2022

Abstract

One of the major applications of Structural Dynamics in Civil, Mechanical, or Aerospace Engineering regards the dynamic characterisation of man-made structures and components. Yet, traditional Experimental Modal Analysis (EMA) needs dedicated setups which may not be always available where and when needed. For these and other reasons, output-only Operational Modal Analysis (OMA) is regarded as a more practical and convenient alternative. Many OMA algorithms have been reported in the scientific literature during the last twenty and more years. In this study, an Automatic OMA method is presented. The proposed algorithm is completely independent of the user experience, fully objective, and based on statistical principles and a Machine Learning (ML) clustering approach. The AOMA code is firstly applied to a numerical case study, to test all the parameters which control the process. An Airbus H135 helicopter blade is then analysed to verify the performance of the algorithm experimentally.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2961336