基于J-EEMD的刀具磨损状态特征提取技术

陈洪涛; 傅攀; 李晓辉; 钟成明

doi:10.13433/j.cnki.1003-8728.2014.0614

基于J-EEMD的刀具磨损状态特征提取技术

doi: 10.13433/j.cnki.1003-8728.2014.0614

1.
西南交通大学,机械工程学院,成都 610031;
2.
东方汽轮机有限公司,德阳 618000

基金项目:

中央高校基本科研业务费专项资金项目(SWJTU12CX039)资助

详细信息

作者简介:
陈洪涛(1967-)，教授，博士研究生，研究方向为切削过程状态监测，scdycht@163.com

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- 文章访问数: 150
- HTML全文浏览量: 25
- PDF下载量: 5
- 被引次数: 0
出版历程
- 收稿日期: 2013-03-14
- 刊出日期: 2015-06-10

Feature Extraction Techniques of Tool Wear States Based on J-EEMD

1.
School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031;
2.
Dongfang Steam Turbine Co.,Ltd.,Deyang 618000

摘要

摘要: 在实际刀具状态监测的过程中,通过传感器所直接测得的数据都包含了大量的噪声信号,因此难以从中获取刀具磨损状态的变化规律,这样显然不利于进行模式识别。应用近似联合对角化下的集合经验模态分解(J-EEMD)对观测信号进行处理,基于信号本身特征,自适应地将切削加工中检测得到的振动和声发射信号分解为多个内蕴模式函数(IMF),然后根据各个IMF之间的能量比对变换,提取出了不同磨损状态下的刀具状态特征。实验证明:在该方法对测得数据进行处理的基础上,能够很好地识别出刀具磨损程度的不同状态。
- 噪声 /
- 特征提取 /
- 分解 /
- 模式识别
Abstract: In the monitoring of cutting tool state, a large number of acoustic noise is contained in the sensor signal.Therefore, it is difficult to get the tool wear state, which is obviously not conducive to pattern recognition. Observedsignals were processed using the method of ensemble empirical mode decomposition based joint approximatediagonalization of eigenmatrice (J-EEMD). In this method, based on the characteristics of the signal itself, thesignals of vibration and acoustic emission were adaptively decomposed into several intrinsic mode functions (IMF);and then transform the energy ratio between the IMF and the original signals of vibrations and acoustic emission;finally the tool state characteristics under different wearing can be extracted. The test result showed that the methodcould preferably help in carrying out the pattern recognition to the different states of tool wear.
- acceleration /
- acoustic emissions /
- acoustic noise /
- backpropagation algorithms

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参考文献(12)

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