Application of variational mode decomposition based on particle swarm optimization in pipeline leak detection

Abstract

Denoising of pipeline leak signals is of great significance to improve the accuracy of pipeline leak detection. Variational mode decomposition (VMD) has the function of signal denoising. However, the inaccurate setting of VMD parameters will affect the result of signal decomposition. This paper proposes a method for optimizing VMD parameters using particle swarm optimization (PSO-VMD). The ratio of the mean and variance of permutation entropy is used as the fitness function of the particle swarm optimization algorithm to search for the optimal number of signal decomposition layers K and penalty factors α. The signal is decomposed using the VMD with the best parameters. Finally, permutation entropy (PE) is used to select the intrinsic modal functions (IMFs) which contains signal characteristics, and these IMFs are used for reconstruction, so as to complete the pipeline signal denoising and leak detection. Experiments show that, compared with the other three denoising methods, the SNR of pipeline signal denoised by the proposed method is increased by 2.1127 on average, MSE and MAE are reduced by 0.000 35 and 0.0043 respectively, and the recognition accuracy of SVM is improved. 5.5%. Therefore, the proposed method has better denoising performance and higher leak detection rate.