The use of pseudo-faults for damage location in SHM: An experimental investigation on a Piper Tomahawk aircraft wing

Abstract

The application of pattern recognition-based approaches in damage localisation and quantification will eventually require the use of some kind of supervised learning algorithm. The use, and most importantly, the success of such algorithms will depend critically on the availability of data from all possible damage states for training. It is perhaps well known that the availability of damage data through destructive means cannot generally be afforded in the case of high value engineering structures outside laboratory conditions. This paper presents the attempt to use added masses in order to identify features suitable for training supervised learning algorithms and then to test the trained classifiers with damage data, with the ultimate purpose of damage localisation. In order to test the approach of adding masses, two separate cases of a dual-class classification problem, representing two distinct locations, and a three-class problem representing three distinct locations, are examined with the help of a full-scale aircraft wing. It was found that an excellent rate of correct classification could be achieved in both the dual-class and three-class cases. However, it was also found that the rate of correct classification was sensitive to the choices made in training the supervised learning algorithm. The results for the dual-class problem demonstrated a comparatively high level of robustness to these choices with a substantially lower robustness found in the three-class case.