Classification of wavelet decomposed AE signals based on parameter-less self organised mapping

Composite materials are characterised by different types of failure mechanisms which are typically associated with matrix cracking, fibre-matrix debonding and fibre breakage. These three mechanisms result in a different AE signature, which can be often recognised. In certain cases it is necessary to classify and map the damage types so as to be able to evaluate the accumulated damage and remaining strength of the material. In this framework, neural networks are widely used for damage characterisation. The classical approach involves recording waveform features and tries to associate them with the underlying damage source. Nevertheless, very often, it is very hard to draw definite conclusions based on these features. In this study, we have used a new type of a neural network which is called parameter-less self organised mapping. It is based on Kohonen neural networks but it is not bound to the naturally subjective learning rate, neighbourhood function and their annealing with the training progress. Moreover, for the training of the NN and the subsequent classification, we have successfully used wavelet decomposed AE signals.