南京林业大学学报(自然科学版) ›› 2011, Vol. 35 ›› Issue (06): 101-106.doi: 10.3969/j.jssn.1000-2006.2011.06.021

• 研究论文 • 上一篇    下一篇

胶合板加载损伤的声发射信号小波包特征提取

徐锋,刘云飞*   

  1. 南京林业大学信息科学技术学院,江苏南京210037
  • 出版日期:2011-11-28 发布日期:2011-11-28
  • 基金资助:
    收稿日期:2011-01-04修回日期:2011-06-14基金项目:南京林业大学“十五”人才基金项目;南京林业大学科技创新基金项目作者简介:徐锋 (1977—),讲师,硕士。*刘云飞(通信作者),教授。Email: lyf@njfu.com.cn。

Feature extraction of acoustic emission signal of plywood in loading by wavelet package transform

XU Feng, LIU Yunfei*   

  1. College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
  • Online:2011-11-28 Published:2011-11-28

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。结果表明:3层胶合板表板断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1、2、3、4和7频段;5层胶合板表板断裂信号频率单一,幅值较高,以膨胀波为主;整板断裂主要以弯曲波模式为主,频率较低,能量多集中于第1、2频段;脱胶和3层板整板断裂信号波形为膨胀波和弯曲波混合型,弯曲波为主,能量多集中于第1、2、3、4频段。实验表明,频谱、小波包时频、小波包能量谱联合分析,能够识别各种加载破坏形式对应的声发射信号特征。

Abstract: To identify the different damage types of plywood, a method based on timefrequency and proportion of energy was proposed for feature extraction of acoustic emission (AE) signal of plywood by combining the waveletpacket timefrequency analysis with energy spectrum. The result showed that dilatational wave and flexural wave were the main modes of fracture signal from threeplywood outer with wide frequency spectrum, and the energy of signal was mainly concentrated in the first, second, third, fourth and seventhband of the wavelet power spectrum. Rupture signals from table and the entire board of five plywood were mainly dominated by dilatational wave and flexural wave mode respectively, and the former frequency was unitary and amplitude was higher, the latter energy mostly focused on the first, second band. Degumming and the entire board fault of three boards signals waveform were composed of dilatational wave and flexural wave and the latter was dominant, and signals energy mostly focused on the first, second, third and fourth band of the wavelet power spectrum. The test results revealed associative analysis of frequency spectrum, timefrequency map and wavelet packet energy spectrum could be used to identify AE signals characteristics corresponding to various loading failure modes respectively.

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