Classification of multi-site damage using support vector machines

Abstract

Pattern recognition is now well-known to be a powerful approach to addressing the higher levels of damage identification e.g. location and severity assessment of damage. However, a major problem in implementation for real structures is the need for training data associated with all possible damage states. Even if appropriate data were available for individual damage states, the combinatorial explosion in states which occurs when multiple simultaneous damages are present would usually prohibit a pattern recognition approach. One approach to the solution of this problem is to construct classifiers on the basis of single damage data which will generalise to multiple damage states; the current paper is a very preliminary step in this direction. In the first part, a comprehensive multiple damage feature database is established as the result of an experimental campaign on a full-sized aircraft wing structure; in the second part, a classifier based on the support vector machine paradigm is investigated. The paper also considers how data visualisation can shed light on which features are likely to generalise best from the single damage problem to the multiple damage case.