Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map
Acoustic emission (AE) source localization has been used to visualize progress failures generated in a wide variety of materials. In the conventional approaches, AE source localization algorithms assume that the AE signal is propagated as a straight line. However, it is supposed that progress failur...
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| Format: | Article |
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MDPI AG
2023-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/9/5745 |
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| author | Katsuya Nakamura Yoshikazu Kobayashi Kenichi Oda Satoshi Shigemura |
| author_facet | Katsuya Nakamura Yoshikazu Kobayashi Kenichi Oda Satoshi Shigemura |
| author_sort | Katsuya Nakamura |
| collection | DOAJ |
| description | Acoustic emission (AE) source localization has been used to visualize progress failures generated in a wide variety of materials. In the conventional approaches, AE source localization algorithms assume that the AE signal is propagated as a straight line. However, it is supposed that progress failures form heterogeneity of elastic wave velocity distributions. Hence, with the conventional source localization, it is expected that the localization accuracy is reduced in heterogeneous materials since diffraction and refraction waves are generated. Thus, if the straight propagation waves are classified, conventional source localizations are performed in the heterogeneous materials. The self-organizing map (SOM) is one of the unsupervised learning methods, and the SOM has potential to classify straight propagation waves for the source localizations. However, the application of classified AE signals in source localization is not popular. If classified AE signals are applied to the time difference of arrival (TDOA) method, which is the popular localization method, it is expected that number of visualized sources are decreased because the algorithm does not consider the selection of the propagated wave. Although ray tracing has potential to localize a larger number of sources than the TDOA method, it is expected that the localized sources are less accurate in comparison with results of the TDOA method. In this study, classified waves were applied to two of the source localizations, and model tests based on pencil-lead breaks (PLBs) generating artificial AE sources were conducted to validate the performance of the source localizations with classified waves. The results of the validation confirmed that the maximum error in the TDOA method is larger in comparison with ray tracing conducted with 20 mm intervals of source candidates. Moreover, ray tracing localizes the same number of sources as the number of PLB tests. Therefore, ray tracing is expected to more practically localize AE sources than the TDOA method if classified waves are applied. |
| first_indexed | 2024-03-11T04:23:28Z |
| format | Article |
| id | doaj.art-a7159b01ecf7428bbfaea30918643327 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T04:23:28Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a7159b01ecf7428bbfaea309186433272023-11-17T22:38:14ZengMDPI AGApplied Sciences2076-34172023-05-01139574510.3390/app13095745Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing MapKatsuya Nakamura0Yoshikazu Kobayashi1Kenichi Oda2Satoshi Shigemura3Department of Civil Engineering, College of Science and Technology, Nihon University, Tokyo 101-8308, JapanDepartment of Civil Engineering, College of Science and Technology, Nihon University, Tokyo 101-8308, JapanDepartment of Civil Engineering, College of Science and Technology, Nihon University, Tokyo 101-8308, JapanDepartment of Civil Engineering, College of Science and Technology, Nihon University, Tokyo 101-8308, JapanAcoustic emission (AE) source localization has been used to visualize progress failures generated in a wide variety of materials. In the conventional approaches, AE source localization algorithms assume that the AE signal is propagated as a straight line. However, it is supposed that progress failures form heterogeneity of elastic wave velocity distributions. Hence, with the conventional source localization, it is expected that the localization accuracy is reduced in heterogeneous materials since diffraction and refraction waves are generated. Thus, if the straight propagation waves are classified, conventional source localizations are performed in the heterogeneous materials. The self-organizing map (SOM) is one of the unsupervised learning methods, and the SOM has potential to classify straight propagation waves for the source localizations. However, the application of classified AE signals in source localization is not popular. If classified AE signals are applied to the time difference of arrival (TDOA) method, which is the popular localization method, it is expected that number of visualized sources are decreased because the algorithm does not consider the selection of the propagated wave. Although ray tracing has potential to localize a larger number of sources than the TDOA method, it is expected that the localized sources are less accurate in comparison with results of the TDOA method. In this study, classified waves were applied to two of the source localizations, and model tests based on pencil-lead breaks (PLBs) generating artificial AE sources were conducted to validate the performance of the source localizations with classified waves. The results of the validation confirmed that the maximum error in the TDOA method is larger in comparison with ray tracing conducted with 20 mm intervals of source candidates. Moreover, ray tracing localizes the same number of sources as the number of PLB tests. Therefore, ray tracing is expected to more practically localize AE sources than the TDOA method if classified waves are applied.https://www.mdpi.com/2076-3417/13/9/5745AE source localizationheterogeneous materialsself-organizing mapunsupervised learning methods |
| spellingShingle | Katsuya Nakamura Yoshikazu Kobayashi Kenichi Oda Satoshi Shigemura Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map Applied Sciences AE source localization heterogeneous materials self-organizing map unsupervised learning methods |
| title | Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map |
| title_full | Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map |
| title_fullStr | Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map |
| title_full_unstemmed | Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map |
| title_short | Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map |
| title_sort | application of classified elastic waves for ae source localization based on self organizing map |
| topic | AE source localization heterogeneous materials self-organizing map unsupervised learning methods |
| url | https://www.mdpi.com/2076-3417/13/9/5745 |
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