摘要
以碳纤维复合材料的分层、孔隙和疏松缺陷的超声波检测信号为研究对象,对包含缺陷信息的复合材料超声波检测信号进行小波包变换,从近似系数及细节系数提取样本的特征值。建立并训练了一种用于实现缺陷识别的BP神经网络,该网络使用Levenberg-Marquardt算法可以快速地完成对数据的处理。使用该网络可进行缺陷类型的识别。
Based on signal of carbon fiber composites defect such as lamination, porosity, looseness in ultrasonic testing , this paper performs Wavelet packet transform on ultrasonic testing signals for carbon fiber composites that contain defect information, extracts sample-features from approximation coefficients and detail coefficients, it builds and trains a BP neural network for defect identification. The network uses Levenberg-Marquardt algorithm to quickly process the data. It identifies the defect type by means of BP neural and achieves good effect.
出处
《无损检测》
北大核心
2007年第8期450-452,460,共4页
Nondestructive Testing
关键词
超声波检测
小波包变换
特征提取
BP神经网络
Ultrasonic testing
Wavelet packet transform
Feature extraction
BP neural network
