State-of-the-Art Review on the Acoustic Emission Source Localization Techniques
he acoustic emission technique has been applied successfully for the identification, characterization, and localization of deformations in civil engineering structures. Numerous localization techniques, such as Modal Acoustic Emission, Neural Networks, Beamforming, and Triangulation methods with or...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2021
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110813615&doi=10.1109%2fACCESS.2021.3096930&partnerID=40&md5=e9a58f455cfee229d9594a113955db30 http://eprints.utp.edu.my/29497/ |
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| Summary: | he acoustic emission technique has been applied successfully for the identification, characterization, and localization of deformations in civil engineering structures. Numerous localization techniques, such as Modal Acoustic Emission, Neural Networks, Beamforming, and Triangulation methods with or without prior knowledge of wave velocity, have been presented. Several authors have conducted in-depth research in the localization of AE sources. However, existing review papers do not focus on the performance evaluation of existing source localization techniques. This paper discusses these techniques based on the number of sensors used and the geometry of the structures of interest. Furthermore, it evaluates them on the basis of their performance. At the end of this paper, a comparative analysis of existing methods has been presented based on their basic principles, key strengths, and limitations. A deep learning circular sensor cluster-based solution has the potential to provide a low-cost reliable localization solution for acoustic emission sources. © 2013 IEEE. |
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