Maximum likelihood estimation for leak localization in water distribution networks using in-pipe acoustic sensing

• Published in: The Journal of the Acoustical Society of America 2023-03, Vol.153 (3_supplement), p.A300-A300
• Main Authors: Agrawal, Pranav, Fong, Stan, Friesen, Dirk, Narasimhan, Sriram
• Format: Article
• Language: English
• ISSN: 0001-4966; 1520-8524
• DOI: 10.1121/10.0018930

With drinking water resources rapidly depleting with time, water conservation efforts have received special emphasis, especially in arid regions like California. One of the major sources of unused water expenditure is inconspicuous leaks in underground water distribution networks (WDN), making it highly essential to quickly detect and localize them. The leak detection and localization problem has been widely studied for a straight pipeline system, however, estimating the leak location in a pipe network remains largely unexplored. In this study, we measure the acoustic pressure signals inside a pipe network at multiple locations using state-of-the-art hydrophone-enabled devices. To localize the leaks in pipe networks, we propose maximum likelihood estimation, which has previously shown high efficacy in localizing mobile devices in a cellular network. In this approach, the cross-correlation of the filtered signals from different sensor pairs yields multiple time delays corresponding to multiple acoustic paths traversed by the leak noise in the pipe network, which is more difficult to solve compared to a straight pipe system. The leak location is then identified by maximizing a conditional probability distribution function of the distance between the sensor and the leak location.