• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2018, Vol. 54 ›› Issue (8): 95-100.doi: 10.3901/JME.2018.08.095

• 仪器科学与技术 • 上一篇    下一篇

CO2气保焊电信号的平移不变量小波消噪

黄勇, 王克鸿, 周晓晓   

  1. 南京理工大学材料科学与工程学院 南京 210094
  • 收稿日期:2017-04-25 修回日期:2017-11-22 出版日期:2018-04-20 发布日期:2018-04-20
  • 通讯作者: 王克鸿(通信作者),男,1963年出生,博士,教授,博士研究生导师。主要研究方向为先进焊接机理、方法和工艺、加工过程智能化数字化、机电一体化设备与机器人智能系统集成等。先后获国家技术发明、国防技术发明、国防科技、江苏省科技进步等二等奖6项,发表论文近百篇。E-mail:wkh1602@126.com
  • 作者简介:黄勇,男,1990年出生,博士研究生。主要研究方向为焊接车间物联网。E-mail:hy08161501@163.com
  • 基金资助:
    兵器预研支撑基金资助项目(JSCG2017606B005)。

Translation Invariant Wavelet De-noising of CO2 Gas Shielded Arc Welding Electrical Signal

HUANG Yong, WANG Kehong, ZHOU Xiaoxiao   

  1. School of Material Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094
  • Received:2017-04-25 Revised:2017-11-22 Online:2018-04-20 Published:2018-04-20

摘要: CO2气保焊的焊接过程中电信号含有大量随机非平稳噪声,消噪预处理是电信号后期分析的重要环节。常见的信号滤波方法有硬件滤波和软件滤波,其中小波阈值消噪方法在软件滤波中应用最为广泛,该方法能够很好的消除电信号中的噪声,但在信号不连续点处易产生伪吉布斯现象。提出利用一种平移不变量小波方法(Translation invariant de-noising,TID)对焊接电信号进行去噪处理,其通过对信号进行多次循环平移,再将平移后的信号进行软(硬)阈值小波消噪处理,然后将消噪后的重构信号进行反向逆平移,最后再对去噪结果进行平均,该方法可消除软阈值小波消噪产生的伪吉布斯(Pseudo-Gibbs)现象。结果表明相对于传统软阈值小波去噪法,该方法去噪后的信噪比更高,去噪后信号更加逼近于真实信号。该方法在熔化极气体保护焊电信号降噪处理方面具有广泛的前景,进一步扩展了小波方法在焊接中的应用。

关键词: CO2气体保护焊, 电信号, 平移不变量, 小波软阈值去噪

Abstract: The electrical signal in the CO2 gas shielded arc welding process contains a large number of random non-stationary noises. De-noising pretreatment is an important part in the analysis of electrical signals. Common signal filtering methods are hardware filter and software filter, in which the wavelet threshold de-noising method is widely used in many wavelet de-noising methods. This method can eliminate the noise in the signal, but it is easy to generate the pseudo-Gibbs phenomenon at the signal discontinuity point. we put forward the method of using translation invariant wavelet to de-noise the welding signal. The method through multiple signal cyclic shift, and soft (hard) wavelet threshold de-noising the shifted signal with soft (hard) wavelet threshold, and then inverse translate the de-noised signal. Finally, the de-noising results are averaged. This method can effectively eliminate the pseudo-Gibbs phenomenon of the traditional soft threshold wavelet de-noising to appear. The result shows that this method can improve the SNR of signal, and the signal de-noising is closer to the true signal. This method has broadly prospects in GMAW electric signal noise reduction processing, and extend the application of wavelet method in welding process.

Key words: CO2 gas shielded arc welding, electrical signal, soft threshold wavelet de-noising, translation invariant

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