基于小波神经网络的FRP容器爆破声发射信号研究

Acoustic Emission Signal Study of FRP Container Blasting Based on the Wavelet Neural Network

对FRP复合材料容器进行水压爆破的力学性能实验,利用声发射检测技术,采集FRP复合材料容器在受压损伤和爆破过程中的声发射信号。将采集到的声发射信号进行小波包特征提取,得到的前8个频带的特征向量作为小波神经网络的输入样本。据得到的数据样本库数据来预测、验证复合材料容器的破坏过程。

Take the mechanical properties of water pressure blasting experiment of FRP composite container,the acoustic emission signal of FRP composite material container in compression injury and in the process of blasting was collected by the acoustic emission testing technology. Wavelet packet is used to extract features of acoustic emission signal collected,feature vectors of the first eight frequency bands as wavelet neural network input samples. According to the sample library data to predict and validate the failure process of composite material container.