Intelligent recognition of acoustic emission signals from damage of glass fiber-reinforced plastics

IF 1.7 4区材料科学 Q3 MATERIALS SCIENCE, COMPOSITES

Advanced Composites Letters Pub Date : 2020-11-26 DOI:10.1177/2633366X20974683

Qiufeng Li, Tiantian Qi, Lihua Shi, Yao Chen, Lixia Huang, Chao Lu

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引用次数: 5

Abstract

Glass fiber-reinforced plastics (GFRP) is widely used in many industrial fields. When acoustic emission (AE) technology is applied for dynamic monitoring, the interfering signals often affect the damage evaluation results, which significantly influences industrial production safety. In this work, an effective intelligent recognition method for AE signals from the GFRP damage is proposed. Firstly, the wavelet packet analysis method is used to study the characteristic difference in frequency domain between the interfering and AE signals, which can be characterized by feature vector. Then, the model of back-propagation neural network (BPNN) is constructed. The number of nodes in the input layer is determined according to the feature vector, and the feature vectors from different types of signals are input into the BPNN for training. Finally, the wavelet packet feature vectors of the signals collected from the experiment are input into the trained BPNN for intelligent recognition. The accuracy rate of the proposed method reaches to 97.5%, which implies that the proposed method can be used for dynamic and accurate monitoring of GFRP structures.

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玻璃钢损伤声发射信号的智能识别

玻璃纤维增强塑料(GFRP)广泛应用于许多工业领域。应用声发射技术进行动态监测时，干扰信号往往会影响损伤评估结果，严重影响工业生产安全。本文提出了一种有效的玻璃钢损伤声发射信号智能识别方法。首先，利用小波包分析方法研究了干扰信号和声发射信号在频域的特征差异，并用特征向量对其进行表征;然后，建立了反向传播神经网络(BPNN)模型。根据特征向量确定输入层的节点数，将不同类型信号的特征向量输入到BPNN中进行训练。最后，将实验信号的小波包特征向量输入训练好的bp神经网络进行智能识别。该方法的准确率达到97.5%，可用于GFRP结构的动态准确监测。

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

Advanced Composites Letters 工程技术-材料科学：复合

自引率

0.00%

发文量

审稿时长

4.2 months

期刊介绍： Advanced Composites Letters is a peer reviewed, open access journal publishing research which focuses on the field of science and engineering of advanced composite materials or structures.