Automated Defect Localization via Low Rank Plus Outlier Modeling of Propagating Wavefield Data
This addresses material diagnostics for non-destructive evaluation in scenarios with complex or unknown mechanical models, but it is incremental as it builds on existing low rank plus outlier methods.
The paper tackles the problem of localizing material defects without prior knowledge of structural properties by proposing an agnostic inference strategy that couples spatiotemporal windowing with low rank plus outlier modeling, achieving localization in simulated benchmark problems for point and line defects.
This work proposes an agnostic inference strategy for material diagnostics, conceived within the context of laser-based non-destructive evaluation methods, which extract information about structural anomalies from the analysis of acoustic wavefields measured on the structure's surface by means of a scanning laser interferometer. The proposed approach couples spatiotemporal windowing with low rank plus outlier modeling, to identify a priori unknown deviations in the propagating wavefields caused by material inhomogeneities or defects, using virtually no knowledge of the structural and material properties of the medium. This characteristic makes the approach particularly suitable for diagnostics scenarios where the mechanical and material models are complex, unknown, or unreliable. We demonstrate our approach in a simulated environment using benchmark point and line defect localization problems based on propagating flexural waves in a thin plate.