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Ultrasonic guided waves (GW) can inspect large structures since they can propagate long distances with low attenuation. They are widely used in Structural Health Monitoring (SHM) to extract useful information about the actual state of the component. However, in a finite plate, due to scattering caused by both damage and boundaries, the extraction of damage features from the recorded signals can be tricky and plays a key role in the implementation of this technology on real components. In this paper, starting from a developed damage detection algorithm, the sensitivity of an SHM system, based on GW propagation and piezoelectric (PZT) sensors, is numerically investigated. The test case consists of an aluminium plate equipped with five PZTs. Different damages are modelled outside the PZTs network coverage area, positioning them increasingly distant from the PZTs. Results of the Finite Element (FE) analyses allow to evaluate the effectiveness of the developed damage detection method and also to estimate the threshold distance across which detecting damage is prevented. [ABSTRACT FROM AUTHOR]

Guided wave (GW) propagation is one of the most effective damage identification and Structural Health Monitoring (SHM) techniques. SHM systems can provide numerous benefits in short and long term with respect to inspection and maintenance/repair operations. Typical GW-SHM system employ a piezoelectric (PZT) sensors network to diagnose the state of health of the component, allowing for a quasi-real time monitoring by comparing datasets recorded in two different states of the structure. In this paper, a novel damage detection and localization method is discussed. With respect to the actual state of the art, the developed algorithm can locate damages inside and outside the area covered by the PZTs. To assess its reliability, Finite Element (FE) analyses on an aluminium panel, equipped with five surface bonded sensors, have been carried out. Different damage positions have been analysed. The algorithm effectively works in all test cases, resulting in an image that highlights only the faulty area. [ABSTRACT FROM AUTHOR]