基于EEMD的改进小波阈值算法在超声水表中的应用

摘要
图/表
参考文献(16)
相关文章 (15)

全文: PDF (830 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要针对超声水表在实际工作环境中容易受到噪声干扰从而导致计量精度下降的问题，提出了基于集合经验模态分解（EEMD）的改进小波阈值降噪算法。为了提高降噪效果，对小波阈值降噪算法进行了改进，构造了非线性阈值函数取代传统阈值函数，同时给出了一种分解尺度选择的方法。利用EEMD将流速信号分解为一系列的本征模态函数，通过改进小波阈值降噪算法对本征模态函数进行降噪处理，去除其中的噪声分量，为了验证该算法的适用性，将其与小波阈值降噪算法和时空滤波分析方法进行了比较。实验结果表明，以超声水表流速信号为降噪对象时，基于EEMD的改进小波阈值降噪算法具有较好的降噪效果。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	蒋沅1
	2
	上官彪1
	曾竞凯1

关键词 ：集合经验模态分解, 超声水表, 小波阈值降噪, 小波变换, 信噪比

Abstract：In light of the problem that ultrasonic water meters suffer the decrease of measurement accuracy due to susceptibility to noise interference in the actual working environment, an improved wavelet threshold noise reduction algorithm based on ensemble empirical mode decomposition (EEMD) is proposed. To improve the noise reduction effect, the wavelet threshold noise reduction algorithm is improved, a nonlinear threshold function is constructed to replace the traditional threshold function, and meanwhile, a method for selection of the decomposition scale is given. The flow rate signal is decomposed into a series of eigenmode functions using EEMD, and by means of an improved wavelet threshold noise reduction algorithm, the eigenmode function is subjected to noise suppression to remove the noise component. In order to verify the applicability of the algorithm, it is compared with the wavelet threshold noise reduction algorithm and the spatio-temporal filtering analysis method. The experimental results demonstrate that the improved wavelet threshold noise reduction algorithm based on EEMD has a better noise reduction effect on the flow rate signal of the ultrasonic water meter.

Key words： Ensemble empirical mode decomposition Ultrasonic water meter Wavelet threshold denoising Wavelet transform Signal-to-noise ratio

收稿日期: 2020-10-29 出版日期: 2022-03-15

引用本文:

蒋沅1,2，上官彪1，曾竞凯1. 基于EEMD的改进小波阈值算法在超声水表中的应用[J]. 振动与冲击, 2022, 41(5): 208-213.
JIANG Yuan1,2, SHANGGUAN Biao1, ZENG Jingkai1. Application of improved wavelet threshold algorithm based on EEMD in ultrasonic water meter. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(5): 208-213.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I5/208

[1] 周邵萍, 林匡行, 苏永升, 等. 风机振动分析与故障诊断[J]. 华东理工大学学报, 1999(3):104-107.
ZHOU Shaoping,, LIN Kuangxing, SU Yongsheng, et al. Vibration analysis and fault diagnosis on a fan[J]. Journal of East China University of Ence and Technology, 1999(3):104-107.
[2] 张贤达, 保铮. 非平稳信号分析与处理[M]. 北京：国防工业出版社, 1998.
[3] 李士心, 刘鲁源. 小波域中值滤波器设计的研究[J]. 电子科技大学学报, 2003(1):18-21.
LI Shixin, LIU Luyuan. Design of wavelet domain median filter[J]. Journal of University of Electronic Science and Technology of China, 2003(1):18-21.
[4] DONOHO D L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 2002, 41(3):613-627.
[5] 华容, 顾幸生. 基于小波分析旋转机械转子振动信号的相位测量[J]. 华东理工大学学报, 2008, 34(6):887-891.
HUA Rong, GU Xingsheng. Phase Measurement of the rotating machine rotor vibrating signal based on wavelet analysis[J]. Journal of East China University of Science and Technology (Natural ence Edition), 2008, 34(6):887-891.
[6] HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971):903-995.
[7] 赵志宏, 杨绍普, 申永军. 一种改进的EMD降噪方法[J]. 振动与冲击, 2009, 28(12):35-37.
ZHAO Zhihong, YANG Shaopu, SHEN Yongjun. Improved EMD based de-noising method[J]. Journal of Vibration and Shock, 2009, 28(12): 35-37.
[8] ZHANG Genbao, FAN Xiujun. Method for de-nosing based on optimal threshold of EMD optimized by ant colony algorithm[J]. China Mechanical Engineering, 2014, 25(4):427-432.
[9] 邵忍平, 曹精明, 李永龙. 基于EMD小波阈值去噪和时频分析的齿轮故障模式识别与诊断[J]. 振动与冲击，2012, 31(08):96-101+106.
Shao Renping, Cao Jingming, Li Yonglong. Gear fault pattern recognition and diagnosis based on EMD wavelet threshold denoising and time-frequency analysis[J]. Journal of Vibration and Shock, 2012,31 (08): 96-101+106.
[10] 荆刚. 超声流量测量关键技术研究[D]. 山东科技大学，2007.
Jing Gang. Research on key technologies of ultrasonic flow measurement[D]. Shandong University of Science and Technology, 2007
[11] 崔锦泰. 小波分析导论[M]. 西安交通大学出版社, 1995.
Cui Jintai. Introduction to Wavelet Analysis[M]. Xi'an Jiaotong University Press, 1995.
[12] Fábio M. Bayer, Alice J. Kozakevicius, Renato J. Cintra. An Iterative Wavelet Threshold for Signal Denoising[J]. Signal Processing, 2019, 162:10-20.
[13] Xu Xiaobin, Luo Minzhou, Tan Zhiying, et al. Echo Signal Extraction Method of Laser Radar based on Improved Singular Value Decomposition and Wavelet Threshold Denoising[J]. Infrared Physics & Technology, 2018, 92:327-335.
[14] 崔华, 宋国乡. 基于小波阈值去噪方法的一种改进方案[J]. 现代电子技术, 2005, 28(01):8-10.
Cui Hua, Song Guoxiang. An improved scheme based on wavelet threshold denoising method[J]. Modern Electronic Technology, 2005,28 (01): 8-10.
[15] WangTong, Zhang Mingcai, Yu Qihao, et al. Comparing the applications of EMD and EEMD on time–frequency analysis of seismic signal[J]. Journal of Applied Geophysics, 2012, 83:29-34.
[16] 陈可, 李野, 陈澜. EEMD分解在电力系统故障信号检测中的应用[J]. 计算机仿真, 2010, 27(03):263-266.
Chen Ke, Li Ye, Chen Lan. Application of EEMD decomposition in Power system Fault signal Detection[J]. Computer Simulation, 2010, 27 (03): 263-266