This paper deals with the problem of structural health monitoring of tie-rods, which undergo to large changes of eigenfrequencies when temperature changes because of the consequent change of the axial load. An approach for shortening the training period of the monitoring algorithm is proposed, relying on principal component analysis. This new method is compared to a state-of-the-art algorithm to evidence its strengths.
A short-training method for monitoring axially-loaded beams in presence of unknown and large thermal variations / Berardengo, M.; Luca, F.; Vanali, M.; Annesi, G.; Manzoni, S.. - 29:7(2024). (Intervento presentato al convegno 11th European Workshop on Structural Health Monitoring, EWSHM 2024 tenutosi a deu nel 2024) [10.58286/29590].
A short-training method for monitoring axially-loaded beams in presence of unknown and large thermal variations
Vanali M.;
2024-01-01
Abstract
This paper deals with the problem of structural health monitoring of tie-rods, which undergo to large changes of eigenfrequencies when temperature changes because of the consequent change of the axial load. An approach for shortening the training period of the monitoring algorithm is proposed, relying on principal component analysis. This new method is compared to a state-of-the-art algorithm to evidence its strengths.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
