Tool condition monitoring based on fractal and wavelet analysis by acoustic emission

被引：0

作者：

Song, Wanqing ^{[1
]}

Yang, Jianguo ^{[1
]}

Qiang, Chen ^{[2
]}

机构：

[1] Donghua Univ, Coll Mech Engn, 2999 Renmin N Rd,Songjiang Dist, Shanghai 201620, Peoples R China

[2] Shanghai Univ Engn & Sci, Comp Ctr, Shanghai 201620, Peoples R China

来源：

COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2007, PT 1, PROCEEDINGS | 2007年 / 4705卷

基金：

上海市自然科学基金;

关键词：

fractal dimension; wavelets; correlation dimension; acoustic emission; tool condition monitoring;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this article, a technique based on the acoustic emission (AE) signal fractal and wavelet analysis are proposed for tool condition monitoring. it is difficult to obtain an effective result by these raw acoustic emission data. The local characterize of frequency band, which contains the main energy of AE signals, is depicted by the wavelet multi-resolution analysis, fractal dimension can describe the complexity of time series. It is found that the fault signal can effectively be extracted by wavelet transform and fractal dimension. Experimental results prove that this method is effectively.

引用

页码：469 / +

页数：3

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