Ultrasonic Guided Waves (UGW) are widely used in Structural Health Monitoring (SHM) due to their ability to inspect large areas with minimal sensor instrumentation. However, the acquired signals can be challenging to interpret, as they are highly sensitive to material properties, environmental factors and operating conditions. To enhance interpretability and comparability, simplifying these signals into dimensionless quantities is crucial. This study employs finite element (FE) method to model cracks in a thin aluminum panel, aiming to identify the most effective post-processing technique for UGW signals acquired by a network of piezoelectric sensors distributed across the panel’s surface. Damage indicators in both the frequency and time domains are evaluated based on their correlation with critical crack parameters, such as position and size. The findings contribute to optimizing monitoring techniques for timely and accurate damage diagnosis in thin structures, offering valuable insights for predictive maintenance in SHM applications.
Damage Index Selection For Ultrasonic Guided Waves Based Structural Health Monitoring System
Polverino, Antonio
;Perfetto, Donato;Caputo, Francesco;De Luca, Alessandro
2026
Abstract
Ultrasonic Guided Waves (UGW) are widely used in Structural Health Monitoring (SHM) due to their ability to inspect large areas with minimal sensor instrumentation. However, the acquired signals can be challenging to interpret, as they are highly sensitive to material properties, environmental factors and operating conditions. To enhance interpretability and comparability, simplifying these signals into dimensionless quantities is crucial. This study employs finite element (FE) method to model cracks in a thin aluminum panel, aiming to identify the most effective post-processing technique for UGW signals acquired by a network of piezoelectric sensors distributed across the panel’s surface. Damage indicators in both the frequency and time domains are evaluated based on their correlation with critical crack parameters, such as position and size. The findings contribute to optimizing monitoring techniques for timely and accurate damage diagnosis in thin structures, offering valuable insights for predictive maintenance in SHM applications.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


