Automated Detection of Pipe Bursts and Other Events in Water Distribution Systems
Publication: Journal of Water Resources Planning and Management
Volume 140, Issue 4
Abstract
This paper presents a new methodology for the automated near-real-time detection of pipe bursts and other events that induce similar abnormal pressure/flow variations (e.g., unauthorized consumptions) at the district metered area (DMA) level. The new methodology makes synergistic use of several self-learning artificial intelligence (AI) techniques and statistical data analysis tools, including wavelets for denoising of the recorded pressure/flow signals, artificial neural networks (ANNs) for the short-term forecasting of pressure/flow signal values, statistical process control (SPC) techniques for short- and long-term analysis of the pipe burst/other event-induced pressure/flow variations, and Bayesian inference systems (BISs) for inferring the probability of a pipe burst/other event occurrence and raising corresponding detection alarms. The methodology presented here is tested and verified on a case study involving several DMAs in the United Kingdom (U.K.) with both real-life pipe burst/other events and engineered (i.e., simulated by opening fire hydrants) pipe burst events. The results obtained illustrate that it can successfully identify these events in a fast and reliable manner with a low false alarm rate.
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Acknowledgments
This work is part of the first author’s Ph.D. sponsored by the University of Exeter. The data used in the paper have been collected as part of the Neptune project funded by the U.K. Engineering and Physical Sciences Research Council (EP/E003192/1) and provided by Mr Ridwan Patel from Yorkshire Water, who is gratefully acknowledged. The work presented in this paper has been patented (Publication No. WO/2010/131001).
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 140 • Issue 4 • April 2014
Pages: 457 - 467
Copyright
© 2012 American Society of Civil Engineers.
History
Received: May 19, 2012
Accepted: Dec 4, 2012
Published online: Dec 6, 2012
Discussion open until: May 6, 2013
Published in print: Apr 1, 2014
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