Stochastic Reliability Metrics for Damage Characterization Based on Parametric and Voxel-Based Estimation Algorithms

ABSTRACT: To achieve the objectives of condition-based maintenance plus prognosis (CBM+), and realize its potential, the location and size of damage at any length scale, e.g., either a crack or a microstructural perturbation, needs to be determined with statistical metrics to feed prognostic reasoners and risk assessments. Previous work by Victor Technologies has focused on developing estimation-theoretic metrics for model-based inversion algorithms in eddy-current NDE. In this research effort, we will develop and demonstrate a general statistical theory of uncertainty propagation with appropriate metrics, and apply the results to more challenging three-dimensional problems, including those in which sizing and location of flaws are required, as well as materials characterization. This will pave the way for a validation study using benchmark data during Phase II. BENEFIT: The technology that we develop in this proposal will be applicable to the aerospace, nuclear power, materials characterization and many other industries, so our research will have commercial benefits that extend far beyond military applications.