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木塑复合材料缺陷及损伤的声发射信号特征分析及神经网络模式识别
引用本文:殷冬萌,王军,刘云飞.木塑复合材料缺陷及损伤的声发射信号特征分析及神经网络模式识别[J].应用声学,2007,26(6):352-356.
作者姓名:殷冬萌  王军  刘云飞
作者单位:南京林业大学信息科学技术学院,南京,210037
摘    要:针对木塑复合材料五种典型的缺陷及损伤机制,选择合适的木塑试样,应用三点弯曲的加载方法采集声发射信号。对主损伤区附近的声发射事件,应用小波变换提取特征参数,确定五类主要损伤机制所对应的声发射信号特征。采用B—P型反向传播神经网络构成的智能化模式分类器,对此五类声发射信号进行识别,获得了满意的效果。

关 键 词:声发射  小波变换  神经网络  模式识别
修稿时间:2006-12-26

Characteristic analysis and pattern recognition of acoustic emission signals from flaw or damage in wood-plastic composites
YIN Dong-Meng,WANG Jun and LIU Yun-Fei.Characteristic analysis and pattern recognition of acoustic emission signals from flaw or damage in wood-plastic composites[J].Applied Acoustics,2007,26(6):352-356.
Authors:YIN Dong-Meng  WANG Jun and LIU Yun-Fei
Institution:College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037
Abstract:Proper wood-plastic composite samples are chosen for five kinds of typical flaws or damages.Acoustic emission(AE)signals are collected in the three-point flexural tests.AE events near the main damage section are studied.Wavelet transform is ap- plied to extract characteristic parameters from major AE events.An intelligent pattern classifier with B-P neural network is used in recognition of those five kinds of AE signals successfully.
Keywords:Acoustic emission  Wavelet transform  Neural network  Pattern recognition
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