Water Distribution System Burst Detection Using a Nonlinear Kalman Filter
Publication: Journal of Water Resources Planning and Management
Volume 141, Issue 5
Abstract
A water distribution system burst from a sudden pipe failure results in water loss and disruption of customer service. Artificial neural networks, state estimation, and statistical process control (SPC) have been applied to detect bursts. However, system operational condition changes such as the set of operating pumps and valve closures greatly complicates the detection problem. Thus, to date applications have been limited to networks that are supplied by gravity or under consistent operation conditions. This study seeks to overcome these limitations using a nonlinear Kalman filter (NKF) method to identify system condition, estimate system state, and detect bursts.
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Acknowledgments
This material is based in part upon work supported by the National Science Foundation under Grant No. 083590. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. The authors would like to thank Dr. Juan Valdes and Dr. Jian Liu for their initial suggestion of KF for burst detection under system state changes.
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Information & Authors
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Published In
Journal of Water Resources Planning and Management
Volume 141 • Issue 5 • May 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Dec 3, 2013
Accepted: May 27, 2014
Published online: Jul 25, 2014
Discussion open until: Dec 25, 2014
Published in print: May 1, 2015
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