摘要
超声多普勒流量计应用环境较为复杂,针对低、中流速受噪声干扰导致测量精度低和误差大等问题,创新提出了一种基于改进蜣螂算法(ISEDBO)的变分模态分解(VMD)结合奇异值分解(SVD)的降噪模型,以更大程度提高回波信号信噪比。该方法首先利用交叉策略、次优引导控制策略、偷窃行为增强策略优化蜣螂算法,通过对比不同测试函数和其他算法,证明ISEDBO算法的优越性;其次,利用ISEDBO优化VMD参数组合,结合多尺度样本熵(MSE)和频谱系数区分噪声模态分量(IMF);最后,利用SVD对有效IMF分量进行降维重构,进一步克服中低频的二次谐波振荡现象。通过对仿真信号和实验室走车过程的处理分析,多方面验证了方法的可行性,同时与粒子群优化(PSO)、灰狼优化(GWO)、蜣螂优化(DBO)等方法对比,分析ISEDBO-VMD-SVD降噪效果。结果表明:相对模拟信号,ISEDBO-VMD能有效地抑制噪声干扰,极大程度地保留了原始信号特征,相较于PSO-VMD、GWO-VMD、DBO-VMD,信噪比最高达18.78 dB,波形相关系数最高达0.987;相对走车实验,对多组信号MSE值进行统计分析,能有效区分原始信号和背景噪声,对比不同流速探测误差,ISEDBO-VMD-SVD最小,范围在0.009~0.02 m/s,为实际水监测工程应用奠定了坚实的基础。
The application environment for ultrasonic Doppler flow meters is quite complex.To address issues such as low and medium flow rates being affected by noise,leading to low measurement accuracy and significant errors,an innovative noise reduction model has been proposed.This model combines Variational Mode Decomposition(VMD)with Singular Value Decomposition(SVD),based on an improved dung beetle optimizer(ISEDBO),to significantly enhance the signal-to-noise ratio of the echo signals.The method first optimizes the Dung Beetle Optimizer(DBO)using crossover strategies,suboptimal guidance control strategies,and theft behavior enhancement strategies.By comparing different test functions and other algorithms,the superiority of the ISEDBO algorithm is demonstrated.Secondly,the ISEDBO algorithm optimizes the VMD parameter combination,integrating Multi-scale Sample Entropy(MSE)and spectral coefficients to distinguish Intrinsic Mode Functions(IMF).Finally,SVD is used to perform dimensionality reduction and reconstruction on the effective IMF components,further overcoming the secondary harmonic oscillation phenomenon in the mid-low frequency range.Through the processing and analysis of simulation signals and laboratory towing experiments,the feasibility of the method is verified from multiple perspectives.Additionally,the ISEDBO-VMD-SVD noise reduction effect is compared with methods such as particle swarm optimization(PSO),grey wolf optimization(GWO),and dung beetle optimizer(DBO).The results show that,compared to simulated signals,ISEDBO-VMD can effectively suppress noise interference and significantly preserve the original signal characteristics.Compared to PSO-VMD,GWO-VMD,and DBOVMD,it achieves a signal-to-noise ratio of up to 18.78 dB and a waveform correlation coefficient of up to 0.987.In the comparative towing experiment,statistical analysis of the MSE values for multiple signal groups effectively distinguishes the original signal from background noise.When comparing detection errors at different flow rates,ISEDBO-VMD−SVD has the smallest error,ranging from O.009~O.O2 m/s,which provides a solid foundation for practical water monitoring applications.
作者
沈宏学
商信华
牛亚坤
SHEN Hong-xue;SHANG Xin-hua;NIU Ya-kun(Zhengzhou Electronic Information Vocational and Technical College,Zhengzhou 451450,Henan Province,China;Xinyang Agriculture and Forestry University School of Information Engineering,Xinyang 464000,Henan Province,China;Henan University School of Computer and Engineering,Kaifeng 475000,Henan Province,China)
出处
《节水灌溉》
北大核心
2026年第1期1-8,共8页
Water Saving Irrigation
基金
国家自然科学基金委员会青年基金项目“面向JPEG图像的篡改取证关键技术研究”(62202141)。
关键词
超声多普勒
蜣螂优化算法
变分模态分解
样本熵
奇异值分解
测流信号
降噪
ultrasonic doppler
dung beetle optimizer
variational mode decomposition
sample entropy
singular value decomposition
flow measurement signal
noise reduction
