A machine learning approach for automatic operational modal analysis

doi:10.1016/j.ymssp.2022.108813

A machine learning approach for automatic operational modal analysis

2022-05-01

45

PRE

AI

L

Luca Zanotti Fragonara *

M

Marco Civera

DOI：10.1016/j.ymssp.2022.108813

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Abstract

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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.

Keywords:

Operational modal analysis

AOMA

Stabilization diagram

Machine learning

Ambient vibration

Clustering analysis

Helicopter blade

Structural Health Monitoring

A machine learning approach for automatic operational modal analysis

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