On using robust Mahalanobis distance estimations for feature discrimination in a damage detection scenario. (January 2019)

• Record Type: Journal Article
• Authors: Yeager, Mike; Gregory, Bill; Key, Chris; Todd, Michael
• Other Names: Nguyen Andy guest-editor.; Chan Tommy HT guest-editor.; Zhu Xinqun guest-editor.
• Is Part Of: Structural health monitoring. Volume 18:Number 1(2019)
• Page Start: 245
• Page End: 253
• Publication Date: 2019-01
• Subjects: Structural health monitoring -- fiber Bragg grating -- damage detection -- embedded sensors -- Mahalanobis distance -- robust distances -- robust outlier detection; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/1475921717748878
• Languages: English
• ISSNs: 1475-9217

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.