摘要
针对滚刀振动信号中包含大量噪声,特征难以提取的问题,提出一种经验小波变换(EWT)和奇异谱分析(SSA)联合处理的降噪方法。该方法通过对原信号进行EWT分解得到若干个经验模态分量,应用斯皮尔曼系数将经验模态分量分为信号主导分量和噪声主导分量。通过对噪声主导分量利用SSA方法进一步分解,根据奇异值大小筛选出包含信号特征的分量,解决噪声主导分量中信号特征不易提取问题,最后与信号主导分量进行重构,达到信号降噪目的。分别在仿真信号和滚刀振动信号上进行EWT-SSA联合降噪实验,并与经典小波软阈值降噪和EEMD相关系数降噪进行效果对比,实验结果表明该方法在保留原始信号特征的前提下有效去除噪声分量,降噪效果明显优于经典小波软阈值降噪和EEMD相关系数降噪,其可行性和有效性得到验证。
Aiming at the problem that the vibration signal of the hob contains a lot of noise and the features are difficult to extract,a noise reduction method that combines empirical wavelet transform(EWT)and singular spectrum analysis(SSA)is proposed.In this method,several empirical mode components are obtained by EWT decomposition of the original signal,and Spearman coefficients are used to divide the empirical mode components into signal dominant components and noise dominant components.The noise dominant component is further decomposed by the SSA method,and the components containing signal features are screened out according to the magnitude of the singular value,which solves the problem of difficult extraction of signal features in the noise dominant component,and finally reconstructs with the signal dominant component to achieve the purpose of signal noise reduction.The EWT-SSA joint noise reduction experiment was carried out on the simulation signal and the hob vibration signal respectively,and the effects were compared with the classical wavelet soft threshold noise reduction and EEMD correlation coefficient noise reduction.The test results show that this method is on the premise of retaining the original signal characteristics It effectively removes the noise component,and the noise reduction effect is significantly better than the classic wavelet soft threshold noise reduction and EEMD correlation coefficient noise reduction,and its feasibility and effectiveness have been verified.
作者
骆春林
刘其洪
李伟光
LUO Chunlin;LIU Qihong;LI Weiguang(School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640,China)
出处
《中国测试》
CAS
北大核心
2022年第8期109-116,共8页
China Measurement & Test
基金
国家自然科学基金项目(51875216)
广东省重点领域研发计划(2019B090918003)
广东省自然科学基金项目(2017A050501004)
广东省自然资源厅项目(2020030)。
关键词
滚刀振动信号
经验小波变换
奇异谱分析
斯皮尔曼系数
降噪
hob vibration signal
empirical wavelet transform
singular spectrum analysis
Spearman coefficient
denoising
