Damage detection in aluminum and composite elements using neural networks for Lamb waves signal processing

One of the important factors in the structural health monitoring systems is the amount of data that need to be analysed in real time. This study investigated the use of artificially deteriorated signals of Lamb waves in training the novelty detection (ND) system for the early damage detection. In this system Auto-associative Neural Networks were trained using principal components calculated on the basis of experimentally measured signals. The specimens studied relate to two different materials commonly used in the aerospace industry, i.e. aluminium and glass fibre reinforced polymer. Lamb waves measured in these specimens are a good example that the ND algorithm works correctly in case of simple as well as complex signals. Furthermore, it was found that the designed ND system remained sensitive and robust even when it used raw signals with a relatively low sampling rate, on a fairly narrow time window and even noised signals.