A Machine Learning Approach for Automatic Operational Modal Analysis
Vezio Mugnaini
A Machine Learning Approach for Automatic Operational Modal Analysis.
Rel. Rosario Ceravolo, Luca Zanotti Fragonara. Politecnico di Torino, Master of science program in Civil Engineering, 2019
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Abstract
Structural Health Monitoring (SHM) has been one of the main research topics in the area of civil, mechanical and aerospace engineering for the past few years. Modal parameters and their evolution in time can be used as features and indicators of the damage which a structure is subjected to and might also allow for a prediction of the residual useful life of the same. SHM needs of long time-series of data to be efficient. For this reason, output-only techniques of system identification, which can record data continuously and without the constant supervision of an operator, are particularly suitable for this aim and are typically referred to as Operational Modal Analysis (OMA).
In the output-only techniques, the knowledge of the input is replaced by the assumption that the input is a realisation of a stochastic process (white noise)
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