# Noise Reduction for Water Supply Pipeline Leakage Signals Based on the Black-Winged Kite Algorithm

## TL;DR

This paper introduces a new noise reduction method for detecting water supply pipeline leaks using a novel algorithm inspired by the black-winged kite.

## Contribution

A new adaptive noise reduction method combining BWK, VMD, and WT for improved leak detection in water pipelines.

## Key findings

- The proposed method achieves a higher SNR of 14.2280 dB compared to WT and EMD.
- The method performs best under both high and low SNR conditions in simulations and experiments.
- The algorithm shows strong engineering application value for urban water supply pipeline monitoring.

## Abstract

In order to solve the problem of false alarms and missed alarms in pipeline monitoring caused by a large amount of noise in the negative pressure wave signal collected by pressure sensors, a new pressure signal denoising method based on the black-winged kite algorithm (BWK) is proposed. First, the variational mode decomposition (VMD) parameters are optimized through BWK. Next, the effective modal components are screened by sample entropy, and the secondary noise reduction of the signal is carried out by using the wavelet thresholding (WT). Finally, the signal is reconstructed to achieve noise reduction. Simulation experiments show that, compared with WT and empirical mode decomposition (EMD), the method proposed in this paper can achieve the best noise reduction effect under both high and low signal-to-noise ratio (SNR) conditions. The method proposed in the paper can achieve the highest SNR of 14.2280 dB, compared to WT’s SNR of 12.6458 dB and EMD’s SNR of 5.5292 dB. To further validate the performance of the algorithm, an experimental platform for simulating pipeline leaks is built. Compared with WT and EMD, the method proposed in this paper also shows the best noise reduction effect. This method provides a high-precision and adaptive solution for leak detection in urban water supply pipelines and has strong engineering application value.