Open Access

ARTICLE

Research on Leak Location Method of Water Supply Pipeline Based on MVMD

by Qiansheng Fang, Haojie Wang, Chenlei Xie^*, Jie Chen

Anhui Province Key Laboratory of Intelligent Building and Building Energy Saving, Anhui Jianzhu University, Hefei, 230022, China

* Corresponding Author: Chenlei Xie. Email:

(This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)

Computer Modeling in Engineering & Sciences 2023, 134(2), 1237-1250. https://doi.org/10.32604/cmes.2022.021131

Received 29 December 2021; Accepted 18 March 2022; Issue published 31 August 2022

Abstract

At present, the leakage rate of the water distribution network in China is still high, and the waste of water resources caused by water distribution network leakage is quite serious every year. Therefore, the location of pipeline leakage is of great significance for saving water resources and reducing economic losses. Acoustic emission technology is the most widely used pipeline leak location technology. The traditional non-stationary random signal de-noising method mainly relies on the estimation of noise parameters, ignoring periodic noise and components unrelated to pipeline leakage. Aiming at the above problems, this paper proposes a leak location method for water supply pipelines based on a multivariate variational mode decomposition algorithm. This method combines the two parameters of the energy loss coefficient and the correlation coefficient between adjacent modes, and adaptively determines the decomposition mode number K according to the characteristics of the signal itself. According to the correlation coefficient, the effective component is selected to reconstruct the signal and the cross-correlation time delay is estimated to determine the location of the pipeline leakage point. The experimental results show that this method has higher accuracy than the cross-correlation method based on VMD and the cross-correlation method based on EMD, and the average relative positioning error is less than 2.2%.

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