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Proceedings Paper

Pattern Recognition In Acoustic Emission Experiments
Author(s): R. K. Elsley; L. J. Graham
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Paper Abstract

Pattern recognition methods are described for classifying acoustic emission (AE) signals according to their source types. Simple time and frequency domain features of the AE waveforms are used in the classification to distinguish one type from another. Methods for classification using labeled waveforms, and clustering using unlabeled waveforms have been developed and applied to the detection of a fatigue crack growing from a fastener hole in a simulated aircraft structure. Sources of AE in this monitoring application are crack growth, crack face rubbing, fastener fretting, mechanical impacts, electrical transients, and hydraulic noise. Classification of labeled data to separate crack-related AE from the other types produced a 96-100% accuracy, and clustering of unlabeled data pro-duced an 82-94% accuracy. A system calibration method needs to be developed before the pattern recognition algorithms can reliably accommodate specimen geometry changes.

Paper Details

Date Published: 10 September 1987
PDF: 8 pages
Proc. SPIE 0768, Pattern Recognition and Acoustical Imaging, (10 September 1987); doi: 10.1117/12.940279
Show Author Affiliations
R. K. Elsley, Rockwell International Science Center (United States)
L. J. Graham, Rockwell International Science Center (United States)

Published in SPIE Proceedings Vol. 0768:
Pattern Recognition and Acoustical Imaging
Leonard A. Ferrari, Editor(s)

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