CAJ | 학술논문

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。
초성검측무인궤복합재료시건시，침대복합재료교박、초성검측회파신호역발생혼첩적문제，해문제출일충기우경험모식분해화소파포능량보특정적탈점결함식별방법。해방법수선대불동대소탈점결함회파신호진행경험모식분해화소파포분해，분별제취기능량보특정；연후，대제취적능량보특정채용개진대속도함수적경향기신경망락진행탈점결함분류식별。실험결과표명：제취적능량특정능구유효제고불동탈점면적결함적식별솔。
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.