A Deep Learning Framework for Acoustic Emission Sources Localization and Characterization in Complex Aerospace Panels-Reference-Cited by-同舟云学术

A Deep Learning Framework for Acoustic Emission Sources Localization and Characterization in Complex Aerospace Panels

Published:2021-04-01 Issue:4 Volume:79 Page:391-400
ISSN:0025-5327
Container-title:Materials Evaluation
language:
Short-container-title:ME

Author:

Ebrahimkhanlou Arvin,Schneider Melanie,Dubuc Brennan,Salamone Salvatore

Abstract

This paper presents a data-driven approach based on deep stacked autoencoders for the localization and characterization of acoustic emission sources in complex aerospace panels. The approach leverages the multimodal and dispersive reverberations of acoustic emissions. The approach is validated by Hsu-Nielsen pencil lead break tests on a fuselage section of a Boeing 777 instrumented with a single piezoelectric sensor.

Funder

Office of Naval Research Global

Publisher

The American Society for Nondestructive Testing, Inc.

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

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