基于VMD-SVD自优化的管道微泄漏信号增强方法

Weak signal enhancement based on self-optimizing VMD-SVDfor leak location in water-supply pipeline

针对复杂环境噪声下管道微泄漏特征难以提取的问题,提出基于变分模态分解-奇异值分解(VMD-SVD)自优化的管道微泄漏信号增强方法。首先,采用遗传迭代算法对VMD参数对[α,K]进行自适应优化,采用奇异值峭度差频谱对SVD重构阶次进行自适应优化;然后,采用参数优化的VMD对泄漏信号进行分解,并采用峭度分析法对分解的模态分量进行筛选并重构;最后,采用阶次优化的SVD对重构信号进行非线性滤波,从而提高微泄漏信号的信噪比。仿真与实验结果表明,信号增强方法使仿真信号的信噪比提高了9.32 dB,使管道微泄漏信号的相关性提高了5.92倍,使互相关泄漏相对定位误差减少了14.34%。

Aiming at the problem that it is difficult to extract the early characteristics of pipeline micro-leakage under complex environmental noise,this paper proposed a method based on variational mode decomposition-singular value decomposition(VMD-SVD)self-optimizing pipeline microleakage signal enhancement method.Firstly,the genetic iterative algorithm was used to optimize the VMD parameters[α,k],and the singular value kurtosis difference spectrum was used to optimize the reconstruction order of SVD.Then,the leakage signals were decomposed by VMD with optimized parameters,and the decomposed modal components were screened and reconstructed by kurtosis analysis.Finally,order optimized SVD is used to nonlinear filter the reconstructed signals,so as to improve the signal-to-noise ratio(SNR)of micro-leakage signals.Simulation and experimental results show that:the signal enhancement method proposed in this paper improves the signal-to-noise ratio of simulation signals by 9.32 dB,the correlation of pipeline micro-leakage signals has been increased by 5.92 times,and the relative positioning error of cross-correlation leakage is reduced by 14.34%.