Abstract
The research about online monitoring and leakage automatic location of water distribution networks (WDN) has a wide range of applications that include water resource protection, monitoring, and allocation. Variational mode decomposition (VMD) and cross-correlation (CC) based leakage location is a popular and effective method in WDN. However, the value of K intrinsic mode functions (IMFs) based on VMD decomposition needs to be determined artificially, which affects the separation effect of signal frequency band characteristics directly. Hence, this work proposes an adaptive method to determine the parameter K of leakage vibration signal’s IMFs, which will be applied to automatic leakage location in WDN. Firstly, the number of saddle points in the frequency domain envelope of the sampled signal in different step sizes is calculated. The parameter K is determined according to the curvature change of the number of saddle points and the sampled signal. Finally, the selective IMFs are reconstituted into a new signal, which can determine a leak position using CC based time-delay estimation (TDE). To verify the effectiveness of the proposed algorithm, the different methods based on EMD and Fast ICA are compared. The experimental results demonstrate that the proposed parameter K value adaptive VMD (KVA-VMD) decomposition method is more suitable for leakage location in WDN.
Highlights
Leakage in urban water distribution networks (WDN) results in water resource losses and potential water pollution incidents to invade harmful substances [1, 2]
An adaptive time-delay estimation (TDE) algorithm based on the least mean square (LMS) is proposed in which generalized cross-correlation (GCC) is replaced by an adaptive filter [12, 13]. e filter can be updated iteratively by using the leakage signal’s characteristics, and its correlation function can be obtained directly from the system parameters instead of any prior knowledge
A section of buried water supply pipeline with leak points on the campus is selected to verify the method’s effectiveness. e pipeline diagram is shown in Figure 7(a). e pipeline is a galvanized steel pipe, with a total length of 91.12 meters, a hole diameter of 75 mm, and a buried depth of 65 cm. e pipe’s water supply pressure is about 3.0 MPa, and valves are installed at both ends. e acquisition device for leak signal is the SS-01 sensor developed by Wizepipes Company. e device has a sampling frequency of 5000 Hz, a sampling accuracy of 12 bits, and sampling data of 65536 at a time
Summary
Leakage in urban WDN results in water resource losses and potential water pollution incidents to invade harmful substances [1, 2]. In real leakage location practice, the signal-to-noise ratio (SNR) of sampled data by the sensor is meager due to the influence of environmental noise (car whistle, road vibration, valve noise, and other factors), which directly affects the CC based TDE method’s reliability and errors of leak detection. Us, the method based on empirical mode decomposition (EMD) is proposed, which provides a selection of analysis for nonstationary random signals [20, 21]. E K value adaptive selection method for enhancing VMD performance is proposed in this work, where mode components with high SNR are separated to reduce the automatic leak location error in WDN. E experiment was conducted to compare the proposed parameter K value adaptive VMD leak location errors with CC and LMS-TDE. Where δ(t) is the impact function and uk(t) u1 (t), u1(t), . . . uK(t)} represents the K IMFs which are
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