A deep learning-based framework for two-step localization and characterization of acoustic emission sources in metallic panels using only one sensor

Arvin Ebrahimkhanlou; Brennan Dubuc; Salvatore Salamone

doi:10.1117/12.2514228

1 April 2019 A deep learning-based framework for two-step localization and characterization of acoustic emission sources in metallic panels using only one sensor

Arvin Ebrahimkhanlou, Brennan Dubuc, Salvatore Salamone

Author Affiliations +

Proceedings Volume 10972, Health Monitoring of Structural and Biological Systems XIII; 1097209 (2019) https://doi.org/10.1117/12.2514228
Event: SPIE Smart Structures + Nondestructive Evaluation, 2019, Denver, Colorado, United States

Abstract

This study focuses on localizing and characterizing acoustic emission (AE) sources in metallic panels with rivetconnected 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.

Citation Download Citation

Arvin Ebrahimkhanlou, Brennan Dubuc, and Salvatore Salamone "A deep learning-based framework for two-step localization and characterization of acoustic emission sources in metallic panels using only one sensor", Proc. SPIE 10972, Health Monitoring of Structural and Biological Systems XIII, 1097209 (1 April 2019); https://doi.org/10.1117/12.2514228

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