Best practices for evaluating the capability of nondestructive evaluation (NDE) and structural health monitoring (SHM) techniques for damage characterization

Abstract

A comprehensive approach to NDE and SHM characterization error (CE) evaluation is presented that follows the framework of the 'ahat-versus-a' regression analysis for POD assessment. Characterization capability evaluation is typically more complex with respect to current POD evaluations and thus requires engineering and statistical expertise in the model-building process to ensure all key effects and interactions are addressed. Justifying the statistical model choice with underlying assumptions is key. Several sizing case studies are presented with detailed evaluations of the most appropriate statistical model for each data set. The use of a model-assisted approach is introduced to help assess the reliability of NDE and SHM characterization capability under a wide range of part, environmental and damage conditions. Best practices of using models are presented for both an eddy current NDE sizing and vibration-based SHM case studies. The results of these studies highlight the general protocol feasibility, emphasize the importance of evaluating key application characteristics prior to the study, and demonstrate an approach to quantify the role of varying SHM sensor durability and environmental conditions on characterization performance.