摘要
为区分VMD(Variational Mode Decomposition)分解后高低频段模态分量,提高VMD算法的去噪效果,提出一种基于云相似度测量的VMD去噪方法。首先,对信号进行VMD分解,通过计算各个模态分量与信号之间的云相似度值,区分有效分量与噪声分量,然后对噪声分量进行小波滤波,最后将滤波后的分量与有效分量进行重构。通过仿真和实际实验,将提出的去噪法与基于相关系数的VMD去噪法和基于互信息的VMD去噪法对噪声信号进行处理,该方法去噪后所得信号信噪比相对较高,为28.2141 dB,均方误差相对较低,为6.12×10~4,验证了该方法去噪效果的优越性和对油气管道泄漏信号去噪的可行性。
In order to distinguish the high frequency and low frequency modal components after VMD(Variational Mode Decomposition)decomposition and improve the de-noising performance of VMD algorithm,a de-noising method based on cloud similarity measurement is proposed.Firstly,the signal is decomposed by VMD.The effective component and the noisy component are distinguished by calculating the cloud similarity between each modal component and the signal.Then the noisy component is filtered by wavelet transform.Finally,the denoised mode components and the effective component are reconstructed.Through simulation and practical experiments,the proposed denoising method,VMD denoising method based on correlation coefficient and VMD denoising method based on mutual information are used to process the noise signal.The SNR(Signalto-Noise Ratio)obtained by the proposed method is relatively high,which is 28.2141 dB.The mean square error is relatively low,which is 6.12×10~4,which verifies the superiority of the proposed method and the feasibility of denoising the leakage signal of oil and gas pipelines.
作者
周怡娜
路敬祎
董宏丽
张勇
ZHOU Yina;LU Jingyi;DONG Hongli;ZHANG Yong(School of Electrical Engineering and Information,Northeast Petroleum University,Daqing 163318,China;Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control,Northeast Petroleum University,Daqing 163318,China;School of Electronic Science and Technology,Northeast Petroleum University,Daqing 163318,China)
出处
《吉林大学学报（信息科学版）》
CAS
2020年第1期9-17,共9页
Journal of Jilin University（Information Science Edition）
基金
国家自然科学基金资助项目(61873058,61933007,NSFC51575407)
黑龙江省自然科学基金资助项目(ZD2019F001,F2018005)
中国博士后基金资助项目(2017M621242)
中国石油科技创新基金资助项目(2018D-5007-0302)
东北石油大学青年科学基金资助项目(2018QNL-33)
冶金装备及其控制教育部重点实验室基金资助项目(MECOF2019B01,MECOF2019B02).
关键词
云相似度
变分模态分解
信号去噪
cloud similarity
variational mode decomposition
signal de-noising
