[1]刘立君,沈秀强,王晓陆,等.模具修复锤击力信号的dbN小波阈值降噪方法[J].哈尔滨理工大学学报,2019,(04):99-104.[doi:10.15938/j.jhust.2019.04.017]
 LIU Li-jun,SHEN Xiu-qiang,WANG Xiao-lu,et al.Denoising Method of dbN Wavelet Threshold for Die Repair Hammer Force Signals[J].哈尔滨理工大学学报,2019,(04):99-104.[doi:10.15938/j.jhust.2019.04.017]
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模具修复锤击力信号的dbN小波阈值降噪方法()
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《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

卷:
期数:
2019年04期
页码:
99-104
栏目:
材料科学与工程
出版日期:
2019-08-25

文章信息/Info

Title:

Denoising Method of dbN Wavelet Threshold for Die Repair Hammer Force Signals
文章编号:
1007-2683(2019)04-0099-06
作者:
刘立君1沈秀强1王晓陆1杨文浩2姚纪荣3
(1.哈尔滨理工大学 材料科学与工程学院,黑龙江 哈尔滨150080;
2.宁波东浩铸业有限公司,浙江 宁波 315113;3.江苏彤明车灯有限公司,江苏 丹阳 212323)
Author(s):
LIU Li-jun1SHEN Xiu-qiang1WANG Xiao-lu1YANG Wen-hao2YAO Ji-rong3
(1.School of Material Science and Engineering,Harbin University of Science and Technology,Harbin 150080 China;
2.Ningbo Donghao Diecasting CoLtd.,Ningbo 315113,China;3.Jiangsu Tongming Auto Lamp Co, Ltd, Dangyang, 212323, China)
关键词:
锤击力信号小波分解阈值处理信噪比均方根误差
Keywords:
hammer force signal wavelet decomposition threshold processing noise signal ratio root mean square error
分类号:
TN911.72
DOI:
10.15938/j.jhust.2019.04.017
文献标志码:
A
摘要:
针对模具修复锤击力信号采集噪声干扰较大的问题,运用dbN(db为Daubechies的简写,N表示小波阶数)小波对锤击力信号进行小波分解低频系数信号重构。保留信号的低频系数,舍弃高频系数,重构后信噪比为10.077 8,均方根误差为0.6633,初步实现了模具修复锤击力信号的降噪。在dbN小波分解的基础上进行阈值化处理,处理后的信噪比SNR明显提高,最大值为44.2313dB,均方根误差RMSE明显降低,最小值为0.0125。实验结果表明两种方法都能实现对锤击力信号的噪声滤除,其中软阈值法还能较大程度的还原原始信号在突变点处的细节特征,避免了信号的失真,保证了模具修复锤击力信号后续计算准确性。
Abstract:
In order to solve the problem that the noise of the hammer power signal in mold repair is large, the dbN wavelet (abbreviation of Daubechies, N is the wavelet order) is used to reconstruct the hammer force signal according to the obtained low frequency coefficient. The lowfrequency coefficients of the signal are reserved and the highfrequency coefficients are discarded Ultimately, the signaltonoise ratio after noise reduction is 10.0778and the mean square error is 0.6633, the noise reduction of hammer power signal in mold repair is initially achieved. Simultaneously, the thresholding is done on the basis of dbN wavelet decomposition. Experimental results show that the SNR is significantly increased after threshold treatment, up to 39.85dB; the RMSE is significantly reduced and the minimum value is 0.4498. Comprehensive macrowaveform characteristics indicating that the two methods can achieve noise filtering of hammering force signal. Besides, soft threshold methods can also be a greater degree of reduction of the original signal at the mutation point for the details characteristics, to avoid the signal distortion and ensure that the subsequent calculation accuracy of hammering force signal in mold repair.

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备注/Memo

备注/Memo:
?收稿日期: 2017-05-31
基金项目: 哈尔滨市科技创新人才创新基金(2017RAXXJ012);宁波市工业重大专项资助项目(2017B10027);丹阳市丹凤朝阳人才计划项目(20162312);浙江省自然科学基金(LY17E050013)
作者简介:
王晓陆(1992—),男,硕士研究生;
沈秀强(1991—),男,硕士研究生
通信作者:
刘立君(1968—),男,博士,教授,硕士研究生导师,E-mail:888liulijnu@163.com
更新日期/Last Update: 2019-09-03