摘要
针对在强噪声背景中提取核电厂信号有效成分的问题,本文提出一种将完全自适应噪声集合经验模态分解与互相关分析法相结合的降噪方法并进行验证。该方法的主要步骤如下。首先,通过完全自适应噪声集合经验模态分解法对电站信号进行有效分解,得到全部的本征模态分量。然后,根据互相关系数将上述分量进行筛选,得到有用信号主导的分量,将其叠加、重构成降噪后信号。最后,使用降噪指标对降噪效果进行评价。结果表明:与基于经验模态分解、集合经验模态分解的降噪方法相比,本文所提方法得到的降噪后信号信噪比更高、均方根误差更小、相关系数更大、平滑度更好,具有更优的降噪效果。
Aiming at the problem of extracting effective components in the signal of power station with strong background noise,a de-noising method combining the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and the cross-correlation analysis is proposed and verified in this paper.The main steps are as follows.Firstly,the signal of power station is effectively decomposed by the CEEMADN,and all the components of intrinsic mode functions(IMFs)are obtained.Afterwards,the above components are effectively distinguished based on the crosscorrelation coefficients,and the IMFs dominated by the useful signals are obtained.The de-noised signal can be reconstructed by summing these IMFs.Finally,the evaluation indexes of de-noising are used to evaluate the de-noising effect.The results show that compared with the de-noising methods based on the empirical mode decomposition and the ensemble empirical mode decomposition,the denoised signal based on the proposed method in this paper has a higher signal to the noise ratio,a smaller root mean square error,a larger correlation coefficient and a better smoothness,indicating that the denoising effect is better.
作者
刘琳琳
王振宇
李露
陈嘉翊
LIU Linlin;WANG Zhenyu;LI Lu;CHEN Jiayi(State Grid Commercial Big Data CO.,LTD.,Xiongan of Hebei Prov.071799,China)
出处
《核科学与工程》
CAS
CSCD
北大核心
2024年第1期80-90,共11页
Nuclear Science and Engineering
基金
2022年国网数字科技控股有限公司科技项目(1200/2022-87003B)。
关键词
信号降噪
经验模态分解
集合经验模态分解
完全自适应噪声集合经验模态分解
互相关分析
Signal de-noising
Complete ensemble empirical mode decomposition with adaptive noise
Empirical mode decomposition
Ensemble empirical mode decomposition
Cross-correlation analysis
