Abstract

Carbon fiber reinforced plastic (CFRP) structures are vulnerable to low-speed impacts, which will lead to almost invisible impact damage. Therefore, the timely localization of impact is of great significance to damage detection and maintenance of the structure. In this article, a low velocity impact supervisory and testing system based on fiber Bragg grating (FBG) sensors was built up for CFRP laminates to obtain the low velocity impact strain sensitivity model. Meanwhile, genetic algorithm was applied to optimize the configuration of the FBG sensing network. The eigenvectors of the impact signals were extracted by applying fast Fourier transform (FFT) transform and principal component analysis (PCA) technology used as the input of the back propagation (BP) neural network model, while the corresponding impact coordinates were used as the output, to train the model. After training, the impact position prediction model based on BP neural network was obtained, thereby achieving the impact localization for CFRP laminates successfully with an average localization error of 2.1 cm.

摘要

碳纤维增强塑料（CFRP）结构容易受到低速冲击，这将导致几乎看不见的冲击损坏。因此，及时定位冲击对结构的损伤检测和维修具有重要意义。本文针对CFRP层合板搭建了基于光纤布拉格光栅(FBG)传感器的低速冲击监测和测试系统，得到了低速冲击应变敏感模型。同时，应用遗传算法优化光纤光栅传感网络的配置。应用快速傅里叶变换（FFT）变换和主成分分析（PCA）技术提取冲击信号的特征向量作为反向传播（BP）神经网络模型的输入，而相应的冲击坐标作为输出, 训练模型。经过训练，得到了基于BP神经网络的冲击位置预测模型，成功实现了对CFRP层合板的冲击定位，平均定位误差为2.1 cm。