现代电子技术2011，Vol.34Issue(21)：96-99,102,5.
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
摘要
Abstract
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 dominated by dilatational wave and flexural wave mode respectively, the former frequency is unitary and amplitude is higher, the latter energy mostly focus on the first, second band. Degumming signal waveform are composed of dilatational wave and flexural 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Key words
plywood/ acoustic emission/ wavelet package transform/ neural network分类
信息技术与安全科学引用本文复制引用
徐锋,赵明忠,刘云飞..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[J].现代电子技术,2011,34(21):96-99,102,5.基金项目
南京林业大学科技创新基金(163070080) （163070080）
南京林业大学"十五"人才基金(163070505) （163070505）
