Acoustic Emission Source Localization using Deep Transfer Learning and Finite Element Modeling–based Knowledge Transfer

IF 0.5 4区 材料科学 Q4 MATERIALS SCIENCE, CHARACTERIZATION & TESTING
Xuhui Huang, Obaid Elshafiey, Karim Farzia, L. Udpa, Ming Han, Y. Deng
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引用次数: 0

Abstract

This paper presents a novel data-driven approach to localize two types of acoustic emission sources in an aluminum plate, namely a Hsu-Nielsen source, which simulates a crack-like source, and steel ball impacts of varying diameters acting as the impact source. While deep neural networks have shown promise in previous studies, achieving high accuracy requires a large amount of training data, which may not always be feasible. To address this challenge, we investigated the applicability of transfer learning to address the issue of limited training data. Our approach involves transferring knowledge learned from numerical modeling to the experimental domain to localize nine different source locations. In the process, we evaluated six deep learning architectures using tenfold cross-validation and demonstrated the potential of transfer learning for efficient acoustic emission source localization, even with limited experimental data. This study contributes to the growing demand for running deep learning models with limited capacity and training time and highlights the promise of transfer learning methods such as fine-tuning pretrained models on large semi-related datasets.
基于深度迁移学习和有限元建模知识迁移的声发射源定位
本文提出了一种新的数据驱动方法来定位铝板中的两种类型的声发射源,即模拟裂纹状声源的Hsu Nielsen声发射源和作为冲击源的不同直径的钢球冲击。虽然深度神经网络在以前的研究中已经显示出了前景,但要实现高精度需要大量的训练数据,这可能并不总是可行的。为了应对这一挑战,我们研究了迁移学习的适用性,以解决训练数据有限的问题。我们的方法包括将从数值建模中学到的知识转移到实验领域,以定位九个不同的源位置。在此过程中,我们使用十倍交叉验证评估了六种深度学习架构,并证明了迁移学习在有效定位声发射源方面的潜力,即使实验数据有限。这项研究有助于满足对在有限容量和训练时间下运行深度学习模型的日益增长的需求,并强调了迁移学习方法的前景,如在大型半相关数据集上微调预训练模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Materials Evaluation
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
35
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.
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