胶合板声发射信号的小波包特征提取及神经网络模式识别  

Wavelet Feature Extraction and Neural Network Pattern Recognition of Plywood Acoustic Emission Signals

作　　者：徐锋[1] 赵明忠[1] 刘云飞[1] 

机构地区：[1]南京林业大学信息科学技术学院,江苏南京210037

出　　处：《现代电子技术》2011年第21期96-99,102,共5页Modern Electronics Technique

基　　金：南京林业大学科技创新基金(163070080);南京林业大学"十五"人才基金(163070505)

摘　　要：为识别胶合板的不同损伤类型,将小波包时频分析与能量谱相结合,提出基于时频和频段能量占比的胶合板损伤声发射信号特征提取方法。研究得出胶合板基体开裂信号以膨胀波和弯曲波模式并举,频谱较宽,能量主要集中在小波能量谱的第一、二、三、四和七频段;分层信号频率单一,幅值较高,并以膨胀波为主;纤维断裂主要以弯曲波模式为主,频率较低;脱胶信号波形为膨胀波和弯曲波的混合型,以弯曲波为主,能量多集中于第一、二、三、四频段。用小波包提取的能量占比作为由BP神经网络构成的智能化模式分类器的输入样本,对4种声发射信号进行识别,正确率达到92.6%。To identify the different damage types of plywood, a feature extraction method of plywood acoustic emission signal based on time-frequency and proportion of energy is proposed by combining wavelet-packet time-frequency analysis with energy spectrum. The research indicates that dilatational wave and flexural wave are main modes of plywood matrix cracks signal with wide frequency spectrum, and the energy of signal is mainly concentrated in the first, second, third, fourth and seventh-band of the wavelet power spectrum. Delamination and fiber fracture signals of five-story plywood are mainly domina- ted by dilatational wave and flexural wave mode respectively, the former frequency is unitgry and amplitude is higher, the latter energy mostly focus on the first, second band. Degumming signal waveform are composed of dilatational wave and flex- ural wave, and the flexural wave is dominant, whose signal energy focus on the first, second, third and fourth band of the wavelet power spectrum. An intelligent pattern classifier with BP neural network was used in recognition of those four kinds of AE signals, the recognition accuracy of flaws amounted to 92.6%.

关 键 词：胶合板 声发射 小波包变换 神经网络 

分 类 号：TN911.7[电子电信—通信与信息系统] TB529[电子电信—信息与通信工程]