Extracting Information from Conventional AE Features for Fatigue Onset Damage Detection in Carbon Fiber Composites

Extracting Information from Conventional AE Features for Fatigue Onset Damage Detection in Carbon Fiber Composites
Runar Unnthorsson, Niels Henrik Pontoppidan, Magnus Thor Jonsson
Abstract	We have analyzed simple data fusion and preprocessing methods on Acoustic Emission measurements of prosthetic feet 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.
Keywords	Acoustic Emission; Carbon fibres; Data fusion;
Type	Conference paper [With referee]
Conference	59th meeting of the Society for Machinery Failure Prevention Technology
Year	2005 Month April
Publisher	Society for Machinery Failure Prevention Technology
Address	1877 Rosser Lane, Winchester, VA 22601, US
Electronic version(s)	[pdf]
BibTeX data	[bibtex]
IMM Group(s)	Intelligent Signal Processing