Journal
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
Volume 141, Issue 5, Pages -Publisher
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)WR.1943-5452.0000464
Keywords
Water distribution systems; Statistics; Kalman filters; Water distribution system (WDS); Burst detection; Statistical process control (SPC); Nonlinear Kalman filter (NKF); Detectability
Categories
Funding
- National Science Foundation [083590]
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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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