摘要
超声检测无人机复合材料试件时,针对复合材料较薄、超声检测回波信号易发生混叠的问题,该文提出一种基于经验模式分解和小波包能量谱特征的脱粘缺陷识别方法。该方法首先对不同大小脱粘缺陷回波信号进行经验模式分解和小波包分解,分别提取其能量谱特征;然后,对提取的能量谱特征采用改进隶属度函数的径向基神经网络进行脱粘缺陷分类识别。实验结果表明:提取的能量特征能够有效提高不同脱粘面积缺陷的识别率。
As the composite material is thin,the ultrasonic echo signals detected aliasing occurs.In order to recognize the defects effectively, an improved method based on empirical mode decomposition and wavelet packet domain energy characteristics is proposed. Firstly, the wavelet packet domain energy characteristics and the energy characteristics of each component after empirical mode decomposition are obtained from the ultrasound echo signal;then the defect is recognized by the radial basis function neural network whose membership degree function is improved. The experimental results indicate that the extracted energy characteristics can improve the rate of the classification on the de-bonding defects of different sizes effectively.
出处
《中国测试》
CAS
北大核心
2015年第11期27-30,共4页
China Measurement & Test
关键词
复合材料
脱粘
识别
经验模式分解
隶属度函数
composites
de-bond
recognition
empirical mode decomposition
membership degree function
