Statistical Analysis of Guided Wave Imaging Algorithms Performance Illustrated by a Simple Structural Health Monitoring Configuration

International audienceGuided Wave-based Structural Health Monitoring (GWs-SHM) system aims at determining the integrity of a wide variety of plate-like structures such as aircraft fuselages, pipes and fuel tanks. It is often based on a sparse grid of piezoelectric transducers for exciting and sensing guided waves (GWs) that under certain conditions interact with damage while propagating. In recent years, various defect imaging algorithms have been proposed for processing GWs signals, and, particularly, for computing an image representing the integrity of the studied structure. The performance of GWs-SHM system highly depends on a signal processing methodology. This paper compares defect localization accuracy of the three state-of-art defect imaging algorithms (Delay-And-Sum, Minimum Variance, and Excitelet) applied to an extensive simulated database of GWs propagation and GWsdefect interaction in aluminum plate under varying temperature and transducers degradation. This study is conducted in order to provide statistical inferences, essential for SHM system performance demonstration