Extracting information from conventional AE features for onset damage detection in carbon fiber composites

We have analyzed simple data fusion and preprocessing methods on Acoustic Emission measurements of prosthetic feets made of carbon fiber reinforced composites. This paper presents the initial research steps; aiming at reducing the time spent on the fatigue test. With a simple single feature probabilistic scheme we have showed that these methods can lead to increased classification performance. We conclude that: the derived features of the TTL count leads to increased classification under supervised conditions. The probabilistic classification scheme was founded on the histogram, however different approaches can readily be investigated using the improved features, possibly improving the performance using multiple feature classifiers, e.g., Voting systems; Support Vector Machines and Gaussian Mixtures.