Coherent, data-driven Lamb wave localization under environmental variations

Lamb waves are powerful tools in nondestructive evaluation and structural health monitoring. Researchers use Lamb waves to detect and locate damage across large areas. To best utilize Lamb waves, they are analyzed through two processing steps: baseline subtraction and velocity calibration. Baseline subtraction removes background information from our data and velocity calibration tunes our algorithms. Yet, in many scenarios, these steps are challenging to implement. Baseline subtraction is challenging due to variable environmental conditions. Velocity calibration is challenging due to multi-modal and dispersive velocity behavior in Lamb waves. To address both challenges, we present two approaches that combine environmental compensation with self-calibrating localization. We discuss temperature compensation strategies based on the scale transform and singular value decomposition. We then integrate these with a localization framework known as data-driven matched field processing. We show these combined appro...