Abstract: 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 factor α of 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.

Key words: variational mode decomposition, particle swarm optimization, chaos, sigmoid function, euclidean distance, pipeline leak detection