改进的PSO-VMD算法及其在管道泄漏检测中的应用

Improved PSO-VMD Algorithm and Its Application in Pipeline Leak Detection

针对变分模态分解(VMD:Variational Mode Decomposition)算法分解后有效模态分量选择困难以及去噪效果不理想等问题,将粒子群(PSO:Particle Swarm Optimization)与VMD算法结合,提出一种基于混沌和Sigmoid函数改进PSO的优化算法。利用改进的PSO算法优化VMD的分解模态数k和惩罚因子α,进行模态分解,然后计算各模态分量概率密度函数与信号概率密度函数之间的欧氏距离(ED:Euclidean Distance),选取有效模态分量重构信号。实验结果表明,该算法与VMD-CORR(Variational Mode Decomposition-Correlation Coeffificient)算法和EMD-ED(Empirical Mode Decomposition-Euclidean Distance)算法相比,仿真信号和实际管道泄漏信号都得到了较好的去噪效果,并验证了其在管道泄漏检测中的有效性。

In view of problems such as the difficulty in selecting effective modal components and the unsatisfactory denoising effect after the decomposition of VMD(Variational Mode Decomposition)algorithm,an optimization algorithm is proposed to improve PSO(Particle Swarm Optimization)by adjusting the inertia weight and the acceleration factor,which combines PSO with VMD algorithm.The improved PSO algorithm is used to optimize the decomposition mode number k and the punishment factorαof VMD and to conduct the decomposition of mode.Then the ED(Euclidean Distance)is calculated between the probability density function of each modal component and the probability density function of the signal,and the effective modal component is selected to reconstruct the signal.Experimental results show that compared with VMD-CORR(Variational Mode Decomposition-Correlation Coeffificient)algorithm and EMD-ED(Empirical Mode Decomposition-Euclidean Distance)algorithm,the proposed algorithm achieves better denoising effect for both simulated signals and actual pipeline leakage signals,which verifies its effectiveness in pipeline leakage detection.