Conference proceeding
Single-sensor acoustic emission source localization in plate-like structures: A deep learning approach
HEALTH MONITORING OF STRUCTURAL AND BIOLOGICAL SYSTEMS XII, v 10600, pp 106001O-106001O-9
01 Jan 2018
Abstract
Acoustic emission (AE) source localization in plate-like structures with geometric features, such as stiffeners, usually requires a large number of sensors. Even without any geometric feature, such approaches are usually accurate only within the convex area surrounded by sensors. This paper proposes a deep learning approach that only requires one sensor and can localize acoustic emission sources anywhere within a metallic plate with geometric features. The idea is to leverage the edge reflections of acoustic waves as well as their multimodal and dispersive characteristics. This deep learning approach consists of three autoencoder layers and a regression layer. The input to the first autoencoder layer is the continuous wavelet transform of AE signals and the output of the regression layer is the estimated coordinates of AE sources. To validate the performance of the proposed approach, Hsu-Nielsen pencil lead break tests were performed on an aluminum plate with a stiffener. The results show that the proposed approach has no blind zone and can localize AE sources anywhere on the plate.
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Details
- Title
- Single-sensor acoustic emission source localization in plate-like structures: A deep learning approach
- Creators
- Arvin Ebrahimkhanlou - Univ Texas Austin, Smart Struct Res Lab SSRL, Dept Civil Architectural & Environm Engn, 10100 Burnet Rd,Bldg 177, Austin, TX 78758 USASalvatore Salamone - The University of Texas at Austin
- Contributors
- T Kundu (Editor)
- Publication Details
- HEALTH MONITORING OF STRUCTURAL AND BIOLOGICAL SYSTEMS XII, v 10600, pp 106001O-106001O-9
- Series
- Proceedings of SPIE
- Publisher
- Spie-Int Soc Optical Engineering
- Number of pages
- 9
- Grant note
- N00014-17-1-2367 / Office of Naval Research
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:000452819700041
- Scopus ID
- 2-s2.0-85048536751
- Other Identifier
- 991021890003204721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Medicine, Research & Experimental
- Optics