Abstract
The paper is concerned with adopting a data-driven approach to damage detection and location on an aerospace structure without recourse to an artificial neural network. Five advanced features are selected, each detecting the removal of only one of five inspection panels on the structure. The features give perfect classification for damage location for single-site damage and 98.1% correct classification for multi-site damage scenarios, using a statistically calculated threshold. However, if the threshold values for two of the five features are altered slightly, 100% correct classification would be possible for single- and multi-site damage.