Data-driven and calibration-free lamb wave source localization with sparse sensor arrays

Most Lamb wave localization techniques require that we know the wave´s velocity characteristics; yet, in many practical scenarios, velocity estimates can be challenging to acquire, are unavailable, or are unreliable because of the complexity of Lamb waves. As a result, there is a significant need for new methods that can reduce a system´s reliance on a priori velocity information. This paper addresses this challenge through two novel source localization methods designed for sparse sensor arrays in isotropic media. Both methods exploit the fundamental sparse structure of a Lamb wave´s frequencywavenumber representation. The first method uses sparse recovery techniques to extract velocities from calibration data. The second method uses kurtosis and the support earth mover´s distance to measure the sparseness of a Lamb wave´s approximate frequency-wavenumber representation. These measures are then used to locate acoustic sources with no prior calibration data. We experimentally study each method with a collection of acoustic emission data measured from a 1.22 m by 1.22 m isotropic aluminum plate. We show that both methods can achieve less than 1 cm localization error and have less systematic error than traditional time-of-arrival localization methods.