基于小波分析—蚁群BP网络的木构件缺陷无损检测

Wood Structure Nondestructive Detection Based on Wavelet Analysis Ant-colony BP Network

针对木材构件的胶缝缺陷,提出一种基于小波分析—蚁群BP网络的木结构无损检测方法,首先采用超声波测试仪对木材试件进行测试,获取测试信号,为消除探伤时由于测试仪增益调节及缺陷尺寸、角度的变化对测试缺陷回波波高的影响,将缺陷信号幅值归一化。利用小波的频域带通特性,将木材构件超声探伤信号分解到不同的频率通道,考察这些分解信号的时频、能量等特性,从中提取出表征原始信号在不同频率通道下的特征参数,并采用蚁群神经网络对小波信号特征参数进行网络训练,检测木材构件胶缝位置。测试结果表明了该方法的有效性。

In view of the wood component glue line defect, a method of wood structure nondestructive detection was proposed based on ant colony BP neural network. The wood specimens was tested to obtain the test signal by ultrasonic testing instrument, in order to eliminate the testing effect of the tester gain control and defect size, angle variation on the test defect echo amplitude, the defect signal amplitude was needed to normalization. The wood component decomposition of ultrasonic signals was de-composite to different frequency channels by the domain band-pass characteristics of the wavelet frequency. By extract characteristic of the original signal in different frequency channels, the ant colony neural network could train the parameters and examine the position of the wood components with defection. The test results show the effectiveness of the proposed method.