On using robust Mahalanobis distance estimations for feature discrimination in a damage detection scenario

Abstract

In this study, a damage detection and localization scenario is presented for a composite laminate with a network of embedded fiber Bragg gratings. Strain time histories from a pseudorandom simulated operational loading are mined for multivariate damage-sensitive feature vectors that are then mapped to the Mahalanobis distance, a covariance-weighted distance metric for discrimination. The experimental setup, data acquisition, and feature extraction are discussed briefly, and special attention is given to the statistical model used for a binary hypothesis test for damage diagnosis. This article focuses on the performance of different estimations of the Mahalanobis distance metric using robust estimates for location and scatter, and these alternative formulations are compared to traditional, less robust estimation methods.