A Multimodal Gated Recurrent Unit Neural Network Model for Damage Assessment in CFRP Composites Based on Lamb Waves and Minimal Sensing-Reference-Cited by-同舟云学术

A Multimodal Gated Recurrent Unit Neural Network Model for Damage Assessment in CFRP Composites Based on Lamb Waves and Minimal Sensing

Published:2024 Issue: Volume:73 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Zhuang Long¹^ORCID,Luo Kai²^ORCID,Yang Zhibo³^ORCID

Affiliation:

1. Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing, China

2. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China

3. National Key Laboratory of Aerospace Power System and Plasma Technology, Xi’an Jiaotong University, Xi’an, China

Funder

National Natural Science Foundation of China

Natural Science Talents Foundation of Shaanxi province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10379118.pdf?arnumber=10379118

Reference36 articles.

1. An efficient Lamb wave-based virtual refined time-reversal method for damage localization in plates using broadband measurements

2. Lamb wave-based damage imaging of CFRP composite structures using autoencoder and delay-and-sum

3. Energy absorption analysis of STF/Kevlar composite fabric based on 3D morphology of impact basin under low-speed impact

4. Lamb wavefield-based monogenic signal processing for quantifying delamination in composite laminates

5. Damage assessment in composite laminates with the Lamb wave factorization method

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