Increasing Probability of Detecting Acoustic Emission Sources Using Artificial Neural Networks

We study the possibilities of reducing the location error of acoustic emission (AE) sources for cases where AE sources are located near AE receiving transducers (AET). Artificial neural networks (ANN) were used to reduce the location error and increase the probability of detecting such sources when...

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Bibliographic Details
Published inRussian journal of nondestructive testing Vol. 58; no. 5; pp. 333 - 341
Main Authors Matvienko, Yu. G., Vasil’ev, I. E., Chernov, D. V., Kozhevnikov, A. V., Mishchenko, I. V.
Format Journal Article
LanguageEnglish
Published Moscow Pleiades Publishing 01.05.2022
Springer Nature B.V
Subjects
Acoustic emission
Acoustic Methods
Algorithms
Artificial neural networks
Broadband
Characterization and Evaluation of Materials
Chemistry and Materials Science
Emission analysis
Materials Science
Metal strips
Neural networks
Parameters
Pulse generators
Rupture tests
Strip steel
Structural Materials
Training
Transducers
artificial neural network
steel strip
probability of detecting source of acoustic signals
linear location
acoustic emission transducer
acoustic emission
pulse generator
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Summary:We study the possibilities of reducing the location error of acoustic emission (AE) sources for cases where AE sources are located near AE receiving transducers (AET). Artificial neural networks (ANN) were used to reduce the location error and increase the probability of detecting such sources when processing AE diagnostic data. The training of the ANN was carried out according to the parameters of location pulses recorded using a pulse generator on the surface of a steel strip. A broadband transducer installed at various distances from the receiving AET was used to generate pulses. After training, the ANN was used to process the results of locating AE sources recorded during rupture tests of a steel strip with stress raisers in the form of holes with a diameter of 5 mm located at a distance of 40 mm from the receiving AET. The probability of detecting AE sources using the ANN according to the parameters of location pulses in the areas of the stress raisers was , i.e., almost 12 times higher than the probability obtained in the case of using the standard algorithm.
ISSN:1061-8309
1608-3385
DOI:10.1134/S1061830922050059