Advanced feature selection for simplified pattern recognition within the damage identification framework

Issue title: International Conference on Structural Engineering Dynamics – ICEDyn 2009

Article type: Research Article

Authors: Manson, G. | Barthorpe, R.J.

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

Note: [] Corresponding author. E-mail: graeme.manson@sheffield.ac.uk

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.

Keywords: Feature selection, damage location, structural health monitoring, neural networks

DOI: 10.3233/SAV-2010-0550

Journal: Shock and Vibration, vol. 17, no. 4-5, pp. 589-599, 2010