Adaptive Signal Extraction Based on RLMD-SVD for Water Pipeline Leakage Localization
Hongjin Liu, Hongyuan Fang, Xiang Yu, Yangyang Xia
Abstract
The acoustic vibration method is an effective water pipe leak detection method, but when the detection pipeline is long, the surrounding environment is noisy or the leaks are small, the leakage signal will be completely submerged in noise, which leads to a huge error in leak location. Therefore, to improve the accuracy of localization, it is very necessary to extract the leak signal from the high-intensity noise. An adaptive leak signal extraction method based on the combination of robust local mean decomposition and singular value decomposition (RLMD-SVD) is proposed for water pipeline leak location. The original signal is decomposed into several product function (PF) components by the RLMD method, and then the effective PFs are selected to reconstruct the signals. Then, the Hankel matrix is constructed and decomposed by SVD, and further noise-reduced signals are obtained by selecting the effective rank order. In this article, the principles and steps of the algorithm are elaborated, and the effectiveness is verified in simulations and experiments. In the simulations, two detection signals with signal-to-noise ratios (SNRs) of −15 dB are improved by 10.26 and 10.37 dB after RLMD processing and further improved by 1.76 and 1.79 dB after SVD processing. After RLMD-SVD processing, the peak of the cross-correlation (CC) function is more obvious and the time-delay is estimated accurately. In the full-size experiment, the location of the leakage can be found accurately after RLMD-SVD processing, and the results of RLMD-SVD processing are more accurate and reliable compared with no processing.