  In the current work, a problem-splitting approach and a scheme motivated by
transfer learning is applied to a structural health monitoring problem. The
specific problem in this case is that of localising damage on an aircraft wing.
The original experiment is described, together with the initial approach, in
which a neural network was trained to localise damage. The results were not
ideal, partly because of a scarcity of training data, and partly because of the
difficulty in resolving two of the damage cases. In the current paper, the
problem is split into two sub-problems and an increase in classification
accuracy is obtained. The sub-problems are obtained by separating out the most
difficult-to-classify damage cases. A second approach to the problem is
considered by adopting ideas from transfer learning (usually applied in much
deeper) networks to see if a network trained on the simpler damage cases can
help with feature extraction in the more difficult cases. The transfer of a
fixed trained batch of layers between the networks is found to improve
classification by making the classes more separable in the feature space and to
speed up convergence.
