Statistically-based damage detection in geometrically-complex structures using ultrasonic interrogation

Abstract

This article introduces a novel approach to damage detection in the context of a structural health monitoring system. A statistical model of the ultrasonic guided wave interrogation process was developed and used to formulate a likelihood ratio test. From the likelihood ratio test, the optimal detector for distinguishing damaged and undamaged states of the structure was derived and found to be a metric related to signal energy. That result was confirmed using a data-driven approach based on using receiver-operating characteristic curves to compare the energy metric to other metrics found in the literature. The data in this study were generated from instrumenting two separate, geometrically complex structures with ultrasonic sensor–actuators. A three-story bolted-frame structure was constructed in the laboratory to test the approach for connections that produce highly uncertain wave paths, with damage being introduced through local impedance changes and bolt loosening. The second test structure was a section of a fuselage rib taken from a commercial aircraft in which holes and cracks were introduced to provide a testbed with a high degree of realism. The detection performance in both structures was quantified and presented. Finally, different sensor fusion strategies were implemented and the ability of such techniques to increase the statistical distinction between damaged and undamaged cases was quantified.