Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ACSSC.2012.6489360 | ACSCC |
Keywords | DocType | ISSN |
Green's function methods,image classification,maximum likelihood estimation,mechanical engineering computing,pipes,reliability,surface acoustic waves,ultrasonic dispersion,Green's function,Lamb wave,ML estimation,MUSIC imaging,backprojection,complex modeling problem,data-driven approach,dispersive multimodal guided acoustic wave,hot water return pipe,image formation,maximum likelihood defect localization,maximum likelihood imaging,pre-defined grid point number,received signal vector,ultrasonic guided wave,Defect Localization,Maximum Likelihood,Non-Destructive Evaluation | Conference | 1058-6393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicholas O'Donoughue | 1 | 44 | 5.02 |
| Joel B. Harley | 2 | 12 | 3.11 |
| Chang Liu | 3 | 0 | 0.34 |
| José M. F. Moura | 4 | 5137 | 426.14 |
| Irving Oppenheim | 5 | 25 | 3.33 |