Latest revision as of 10:32, 15 February 2021

Abstract

Urban water supply and distribution infrastructure is aging rapidly and the frequency of pipe burst increases. These events can be very expensive due to water supply disruptions, and damage to surrounding properties and infrastructures. Therefore, methods of detecting and localizing underground burst events in real time can be very helpful in mitigating these impacts. This paper is a part of the WaterWiSe@SG project in Singapore. In this paper, a cost-effective wireless sensor network was developed for a real-time monitoring, analyzing, and modeling of urban water distribution systems. This paper presents an application of joint time-frequency analysis (JTFA) for detecting events in water distribution pipelines. The idea behind this method is based on the detection of pressure fluctuations induced by the burst. This proposed approach for event detection employs a spectrogram, one of the JTFA approaches. The feasibility of the proposed method is tested through emulated leak-off experiments and validated with monitoring data in an operational system. The results demonstrate that the proposed method has the potential to assist in the management of water infrastructure by monitoring existing conditions and providing real-time feedback in case of the failure.

Document type: Article

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The different versions of the original document can be found in:

• http://dx.doi.org/10.1109/jsen.2014.2358842 under the license https://creativecommons.org/licenses/by-nc-sa

• https://dspace.mit.edu/bitstream/1721.1/111643/1/Thaw%20Tar%20-IEEE%20Sensors.pdf

• http://hdl.handle.net/1721.1/111643,

https://orcid.org/0000-0001-5358-4140 under the license cc-by-nc-sa

• http://xplorestaging.ieee.org/ielx7/7361/6933962/06901220.pdf?arnumber=6901220,

http://dx.doi.org/10.1109/jsen.2014.2358842 under the license http://creativecommons.org/licenses/by-nc-sa/4.0/

• https://dspace.mit.edu/handle/1721.1/111643,

https://ieeexplore.ieee.org/document/6901220,

http://ieeexplore.ieee.org/document/6901220,

https://academic.microsoft.com/#/detail/2317570015