Automated Defect Localization via Low Rank Plus Outlier Modeling of Propagating Wavefield Data

TL;DR

This paper introduces an agnostic, model-free method for defect localization in materials by analyzing wavefield data with low rank plus outlier modeling, effective even with complex or unknown material properties.

Contribution

It develops a novel, model-agnostic approach combining spatiotemporal windowing and low rank plus outlier modeling for defect detection in wavefield data.

Findings

Successfully localized point and line defects in simulated environments

Effective in scenarios with complex or unknown material properties

Demonstrated robustness of the method in benchmark problems

Abstract

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.