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Shock and Vibration
Volume 17, Issue 4-5, Pages 589-599

Advanced Feature Selection for Simplified Pattern Recognition within the Damage Identification Framework

G. Manson and R.J. Barthorpe

Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

Received 18 June 2010; Accepted 18 June 2010

Copyright © 2010 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.