复杂航空航天面板声发射源定位和表征的深度学习框架

IF 0.5 4区材料科学 Q4 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Materials Evaluation Pub Date : 2021-04-01 DOI:10.32548/2021.ME-04179

A. Ebrahimkhanlou, M. Schneider, B. Dubuc, S. Salamone

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引用次数: 4

摘要

本文提出了一种基于深度堆叠自编码器的数据驱动方法，用于复杂航天板声发射源的定位和表征。该方法利用了声发射的多模态和弥散混响。Hsu-Nielsen在波音777机身部分进行了铅笔芯断裂测试，该测试采用了单个压电传感器。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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

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.

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来源期刊

Materials Evaluation 工程技术-材料科学：表征与测试

CiteScore

0.90

自引率

16.70%

发文量

审稿时长

6-12 weeks

期刊介绍： Materials Evaluation publishes articles, news and features intended to increase the NDT practitioner’s knowledge of the science and technology involved in the field, bringing informative articles to the NDT public while highlighting the ongoing efforts of ASNT to fulfill its mission. M.E. is a peer-reviewed journal, relying on technicians and researchers to help grow and educate its members by providing relevant, cutting-edge and exclusive content containing technical details and discussions. The only periodical of its kind, M.E. is circulated to members and nonmember paid subscribers. The magazine is truly international in scope, with readers in over 90 nations. The journal’s history and archive reaches back to the earliest formative days of the Society.