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Proceedings Paper

Classification of acoustic-emission waveforms for nondestructive evaluation using neural networks
Author(s): Roger S. Barga; Mark A. Friesel; Ronald B. Melton
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Paper Abstract

Neural networks were applied to the classification oftwo types ofacoustic emission (AE) events crack growth andfretting a simulated aiiframejoint specimen. Signals were obtainedfromfour sensors at different locations on the test specimen. Multilayered neural networks were trained to classify the signals using the error backpropagation learning algorithm enabling AE events arisingfrom crack growth to be distinguishedfrom those caused by fretting. In thispaper we evaluate the neural network classWcationperformancefor sensor location dependent and sensor location independent training and testing sets. Further we present a new training strategy which signcantly reduces the time required to learn large training sets using the error backpropagation learning algorithm and improves the generalization performance of the network. 1.

Paper Details

Date Published: 1 August 1990
PDF: 12 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21205
Show Author Affiliations
Roger S. Barga, Battelle/Pacific Northwest Lab (United States)
Mark A. Friesel, Battelle/Pacific Northwest Lab (United States)
Ronald B. Melton, Battelle/Pacific Northwest Lab (United States)


Published in SPIE Proceedings Vol. 1294:
Applications of Artificial Neural Networks
Steven K. Rogers, Editor(s)

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