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首页> 外文期刊>Neurocomputing >Identification of acoustic emission sources for structural health monitoring applications based on convolutional neural networks and deep transfer learning
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Identification of acoustic emission sources for structural health monitoring applications based on convolutional neural networks and deep transfer learning

机译：基于卷积神经网络和深度转移学习的结构健康监测应用的声发射来源识别

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In the present work, different types of acoustic emission (AE) sources are identified by means of computational intelligence. The goal is to characterize the type of AE source and to successfully differentiate between sources that are related to an internal damage, such as a fracture initiation, or an external load represented by an elastic impact. A Hsu-Nielsen source (pencil-lead break) and two steel ball impacts of different diameters are selected for the excitation of an aluminum plate equipped with four piezoelectric transducers to record the acoustic emissions. Furthermore, 25 different areas for the AE sources are defined to collect a large database. Three different machine learning architectures are considered, which can predict the type of the AE source. Time domain signals of the acoustic emissions are used for the training of an artificial neural network and a 1D convolutional neural network. Additionally, the wavelet transformation is performed on the captured signals to generate RGB images of the sensor responses and to train a 2D convolutional neural network in combination with deep transfer learning. An error evaluation of each machine learning model is performed to discuss the classification results. The proposed methodology demonstrates that computational intelligence can be applied to accurately identify the type of AE source based on the captured acoustic emission signals.(c) 2021 Elsevier B.V. All rights reserved.
机译：另外，在本工作中，不同类型的声发射（AE）源通过计算智能来识别。我们的目标是表征AE源的类型，并使用有关的内部损坏源，如一个断裂起始，或通过弹性冲击表示的外部负载之间成功地分化。甲许 - 尼尔森源（铅笔铅断）和两个不同直径的钢球的影响被选择用于配有四个压电换能器来记录声​​发射的铝板的激励。此外，对于AE源25个不同地区的定义是收集大量的数据库。三种不同的机器学习架构考虑，这可以预测AE源的类型。声发射的时域信号用于人工神经网络的训练和1D卷积神经网络。此外，小波变换是在捕获的信号执行以产生传感器响应的RGB图像，并与深迁移学习组合来训练2D卷积神经网络。每次进行机器学习模型的误差的评价，讨论的分类结果。所建议的方法表明计算智能可以应用于准确地识别基于捕获的声发射信号AE源的类型。保留（c）中2021爱思唯尔B.V.所有权利。

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