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• W4386490939 abstract "Leaks cause significant water loss in underground water distribution networks, which makes it critical that utilities quickly detect, localize, and repair them. Acoustic leak detection and localization methods using hydrophones and accelerometers are the most studied technology; however, most studies for localizing leaks have focused on simple straight pipe segments using the cross-correlation technique. Leak localization in a network of pipes is significantly more challenging, and this problem remains largely unexplored in the literature. The difficulty arises because the cross-correlation between two acoustic sensor measurements yields multiple time delays corresponding to multiple paths between the acoustic source and the sensors in a network. Hence, the problem of localizing the leak correctly requires taking such multiple paths into account. In this paper, we propose a new method for localizing leaks in a network of pipes. Our method operates on multiple time difference of arrival (TDOA) by calculating the cross-correlation of the signals from different pairs of hydrophone sensors. A conditional probability distribution function is calculated corresponding to each TDOA, and the leak location is found based on the principle of maximum likelihood estimation. We also formally propose a new term called interior points where we define the conditions in which leaks can be pinpointed or only localized to the closest pipe joint. Using simulation studies, the proposed method is shown to accurately pinpoint leaks for the cases when the simulated leak satisfies appropriate conditions. The method is also validated by conducting experiments on a laboratory test bed where a simulated leak is pinpointed to within 10 cm of the actual leak location." @default.
• W4386490939 created "2023-09-07" @default.
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• W4386490939 date "2023-11-01" @default.
• W4386490939 modified "2023-09-27" @default.
• W4386490939 title "Maximum Likelihood Estimation to Localize Leaks in Water Distribution Networks" @default.
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• W4386490939 doi "https://doi.org/10.1061/jpsea2.pseng-1494" @default.
• W4386490939 hasPublicationYear "2023" @default.
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