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Single-Sensor Acoustic Emission Source Localization in Plate-Like Structures Using Deep Learning

Smart Structures Research Group (SSRG), Department of Civil Architectural and Environmental Engineering, The University of Texas at Austin, 10100 Burnet Rd, Bldg. 177, Austin, TX 78758, USA
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This paper is an extended version of the authors’ conference paper published in 11th International Workshop on Structural Health Monitoring (IWSHM), Stanford, CA, USA, 12–14 September 2017, which was selected for and invited to this special issue.
Aerospace 2018, 5(2), 50; https://doi.org/10.3390/aerospace5020050
Received: 27 March 2018 / Revised: 19 April 2018 / Accepted: 19 April 2018 / Published: 1 May 2018
(This article belongs to the Special Issue Selected Papers from IWSHM 2017)
This paper introduces two deep learning approaches to localize acoustic emissions (AE) sources within metallic plates with geometric features, such as rivet-connected stiffeners. In particular, a stack of autoencoders and a convolutional neural network are used. The idea is to leverage the reflection and reverberation patterns of AE waveforms as well as their dispersive and multimodal characteristics to localize their sources with only one sensor. Specifically, this paper divides the structure into multiple zones and finds the zone in which each source occurs. To train, validate, and test the deep learning networks, fatigue cracks were experimentally simulated by Hsu–Nielsen pencil lead break tests. The pencil lead breaks were carried out on the surface and at the edges of the plate. The results show that both deep learning networks can learn to map AE signals to their sources. These results demonstrate that the reverberation patterns of AE sources contain pertinent information to the location of their sources. View Full-Text
Keywords: acoustic emission; guided ultrasonic waves; deep learning; autoencoders; convolutional neural networks; machine learning; edge reflection; reverberation patterns; structural health monitoring acoustic emission; guided ultrasonic waves; deep learning; autoencoders; convolutional neural networks; machine learning; edge reflection; reverberation patterns; structural health monitoring
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Ebrahimkhanlou, A.; Salamone, S. Single-Sensor Acoustic Emission Source Localization in Plate-Like Structures Using Deep Learning. Aerospace 2018, 5, 50.

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