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Spatial Damage Prediction in Composite Materials using Multipath Ultrasonic Monitoring, advanced Signal Feature Selection and combined Classifier-Regression Artificial Neural Network
* 1 , 2
1  University of Bremen, Dept. of Mathematics and Computer Science, Robert Hooke Str. 5, 28359 Bremen, Germany
2  Faserinstitut Bremen E.V., Am Biologischen Garten 2, 28359 Bremen, Germany
Academic Editor: Stefan Bosse


Automated damage detection in Carbon-Fibre and Fibre Metal Laminates is still a challenge. Impact damages are typically not visible from the outside. Different measuring and analysis methods are available to detect hidden damages, e.g., delaminations or cracks. Examples are X-ray computer tomography and methods based on guided ultrasonic waves (GUW). All measuring techniques are characterised by a high-dimensional sensor data, in the case of GUW that is a set of time-resolved signals as a response to a actuated stimulus. We present a simple but powerful two-level method that reduces the input data (time-resolved sensor signals) significantly by a signal feature selection computation finally applied to a damage predictor function. Beside multi-path sensing and analysis, the novelty of this work is a feed-forward ANN posing low complexity and that is used to implement the predictor function that combines a classifier and a spatial regression model.

Keywords: Structural Health Monitoring, Fibre Lamninates, Feature Selection, Neural Networks