1 August 1990 Classification of acoustic-emission waveforms for nondestructive evaluation using neural networks
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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.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Roger S. Barga, Roger S. Barga, Mark A. Friesel, Mark A. Friesel, Ronald B. Melton, Ronald B. Melton, } "Classification of acoustic-emission waveforms for nondestructive evaluation using neural networks", Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21205; https://doi.org/10.1117/12.21205
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