Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/131369
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Type: Journal article
Title: Approach for near-real-time pipe burst detection, localization, and quantification with low data transmission and sampling rates
Author: Du, X.
Zeng, W.
Lambert, M.
Chen, L.
Hu, E.J.
Citation: Journal of Water Resources Planning and Management, 2021; 147(7):04021032-1-04021032-14
Publisher: American Society of Civil Engineers
Issue Date: 2021
ISSN: 0733-9496
1943-5452
Statement of
Responsibility: 
Xiao-xuan Du, Wei Zeng, Martin F. Lambert, Lei Chen and Eric Jing Hu
Abstract: The purpose of the paper is to present an approach for pipe burst detection, localization, and cross-sectional area quantification based on the changes in the discrete harmonic spectrogram and analysis of damping of fluid transients. The amplitude of each resonance response of the burst-induced transient wave in each analyzed time window in the spectrogram is damped differently because of the presence of the burst. A pressure signal processing algorithm for pipe burst detection and estimation has been developed by the authors to explore this specific type of analysis, in conjunction with a predefined gap between each window. Because the window gap can be set to be equal to the data transmission and sampling rates in the analysis to detect and estimate the burst, it can be applied during real-time data monitoring. Unlike other hydraulic transient-based detection methods, the proposed algorithm only requires the Nyquist frequency of the third resonance response as the data transmission and sampling rates, which are significantly lower than the same quantities in commonly used data acquisition systems. The algorithm has been verified both numerically and experimentally.
Rights: © 2021 American Society of Civil Engineers.
DOI: 10.1061/(ASCE)WR.1943-5452.0001380
Grant ID: ARC
Published version: http://dx.doi.org/10.1061/(asce)wr.1943-5452.0001380
Appears in Collections:Aurora harvest 4
Civil and Environmental Engineering publications

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