摘要
提出了一种新的声发射刀具磨损小波判析方法,该方法通过多层小波分解对信号主能量所处频段进行局部特性刻画,利用小波分解系数特征统计值在统计量与刀具状态间建立物理联系。实例表明,该方法能有效地判断刀具状态,比常用的利用神经网络进行状态分析的方法更具有理论直观性与操作的时效性。
A new wavelet analysis method for acoustic emission tool wear distinguish is presented. The local characterize of the frequency band which contains the signal main energy is depicted by the multiplayer wavelet decomposition. Since then, the physical relationship between the wavelet decomposition coefficients＇ statistical value and tool state can be built up. Its validity is proved by the turning experiments, It has much more theory visualizability and maneuverability than the usual prediction method which based on artificial nerve network（ ANN）
出处
《无损检测》
北大核心
2007年第1期12-15,35,共5页
Nondestructive Testing
基金
上海市自然科学基金项目(02ZF14003)
关键词
声发射
刀具磨损检测
小波分析
Acoustic Emission
Tool wear monitoring
Wavelet analysis
