Maximum likelihood defect localization in a pipe using guided acoustic waves

We discuss image formation using Maximum Likelihood (ML) for the localization of defects in pipes. We make use of guided waves (similar to Lamb waves in plates). We utilize a data-driven approach based on a priori measurements of the Green´s function for a pre-defined number of grid points to overcome the complex modeling problem of dispersive, multi-modal guided waves in this environment. We then compute the ML estimate of reflectivity at each pixel given some received signal vector. We compare this approach to both backprojection and MUSIC imaging for the same set of reference and test data. We show that, for synthesized defects in a lab setting, all three approaches successfully image the defects. However, in situ measurements taken on an active hot water return pipe show that only Maximum Likelihood imaging is successful in a realistic operational environment.