Conference proceeding
A deep learning-based framework for two-step localization and characterization of acoustic emission sources in metallic panels using only one sensor
HEALTH MONITORING OF STRUCTURAL AND BIOLOGICAL SYSTEMS XIII, v 10972, pp 1097209-1097209-8
01 Jan 2019
Abstract
This study focuses on localizing and characterizing acoustic emission (AE) sources in metallic panels with rivet-connected doublers. In particular, a deep learning-based framework is proposed that first performs zonal localization with only one sensor and then depending on the zone in which the source occurs, either finds the coordinates of the source or characterize it based on its source-to-rivet distance. The performance of the framework is assessed in typical scenarios in which the training and testing conditions of the deep networks are not identical, and Hsu-Nielsen sources were carried out for validation.
Metrics
Details
- Title
- A deep learning-based framework for two-step localization and characterization of acoustic emission sources in metallic panels using only one sensor
- Creators
- Arvin Ebrahimkhanlou - The University of Texas at AustinBrennan Dubuc - The University of Texas at AustinSalvatore Salamone - The University of Texas at Austin
- Contributors
- P Fromme (Editor)Z Su (Editor)
- Publication Details
- HEALTH MONITORING OF STRUCTURAL AND BIOLOGICAL SYSTEMS XIII, v 10972, pp 1097209-1097209-8
- Series
- Proceedings of SPIE
- Publisher
- Spie-Int Soc Optical Engineering
- Number of pages
- 8
- Grant note
- N00014-17-1-2367 / Office of Naval Research American Society for Nondestructive Testing (ASNT)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:000484721700006
- Scopus ID
- 2-s2.0-85065552069
- Other Identifier
- 991021890014304721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Engineering, Civil
- Engineering, Mechanical
- Materials Science, Multidisciplinary