基于BWO优化VMD联合小波阈值的管道泄漏次声波去噪方法

Research on Infrasound Denoising Method for Pipeline Leakage Based on BWO Optimized VMD Joint Wavelet Thresholding

管道泄漏次声波信号中的干扰噪声影响管道泄漏定位的精准度。提出了一种基于白鲸优化算法(BWO)优化变分模态分解(VMD)联合小波阈值(WT)的管道泄漏次声波去噪方法。针对VMD算法中分解层数K和惩罚因子α的取值对信号分解结果影响较大,利用白鲸优化算法(BWO)对VMD分解的两关键参数进行寻优,获得最优参数组合[K、α],并利用优化后的参数对次声波信号进行VMD分解,获得一系列本征模函数(IMF)分量。通过计算各IMF分量的相关系数来区分噪声IMF分量和有效IMF分量,引入一种改进的小波阈值函数对有效的IMF分量进行去噪处理,再重构去噪后各有效IMF分量,得到去噪后的管道泄漏次声波信号。通过仿真实验,将所提方法与灰狼优化算法(GWO)优化VMD联合小波阈值和麻雀搜索算法(SSA)优化VMD联合小波阈值两种方法对比,所提方法去噪后信号的信噪比分别提高了1.27%、2.01%,表明所提方法的去噪效果具有一定的优越性,为后续管道泄漏计算定位奠定了良好的基础。

Aiming at the fact that the collected infrasound signals of pipeline leakage contain interference noise,which in turn affects the accuracy of pipeline leakage localization,this study suggests a novel approach for denoising infrasound signals from pipeline leaks.The method combines variational modal decomposition(VMD)optimization with wavelet thresholding,employing the beluga optimization algorithm(BWO).Aiming at the large influence of the number of decomposition layers K and the value of penalty factorαin the VMD algorithm on the signal decomposition results,the two key parameters of the VMD decomposition are optimized by using the beluga optimization algorithm(BWO)to obtain the optimal parameter combination[K,α],and the infrasound signal is decomposed by using the optimized VMD algorithm,and a series of intrinsic mode function(IMF)components are obtained.By calculating the correlation coefficient of each IMF component to distinguish the noisy IMF component from the effective IMF component,an improved wavelet threshold function is introduced to denoise the effective IMF component,and then the denoised IMF component is reconstructed to obtain the denoised infrasound signal of pipeline leakage.Through simulation experiments,comparing this paper’s method with the gray wolf optimization algorithm(GWO)to optimize the VMD joint wavelet threshold and the sparrow search algorithm(SSA)to optimize the VMD joint wavelet threshold,the signal-to-noise ratios of signals after denoising of this paper's method are improved by 1.27%and 2.01%respectively,which shows that the denoising effect of this paper’s method has some superiority,and it lays a good foundation for the subsequent calculation and localization of pipeline leakage.It lays a good foundation for the subsequent calculation and localization of pipeline leakage.