scholarly journals Improving the rapidity of responses to pipe burst in water distribution systems: a comparison of statistical process control methods

2014 ◽  
Vol 17 (2) ◽  
pp. 307-328 ◽  
Author(s):  
Donghwi Jung ◽  
Doosun Kang ◽  
Jian Liu ◽  
Kevin Lansey
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Synthetic Data ◽  
Burst Detection ◽  
Statistical Process ◽  
Pipe Burst ◽  
Nodal Pressure

A pipe burst is a major water distribution system failure. Water escapes the network through the break increasing the total flow entering the network. These higher flows, in turn, increase the head losses in pipes and result in lower water pressures at customer taps. This study focuses on burst detection by seeking to identify anomalies in net system demand, pipe flow rates, and nodal pressure heads. Three univariate statistical process control (SPC) methods (the Western Electric Company rules, the cumulative sum (CUSUM) method, and the exponentially weighted moving average [EWMA]) and three multivariate SPC methods (Hotelling T2 method and multivariate versions of CUSUM and EWMA) are compared with respect to their detection effectiveness and efficiency. First, the three univariate methods are tested using real system burst detection and then the six SPC methods are compared using synthetic data. The real application using net system demand shows that burst flows are proportionally too small to be detected. Synthetic data analyses suggest that the univariate EWMA method using nodal pressure provides the highest detectability. The method's long record length helps detect small bursts and avoid false detection. SPC methods require consistent system operations for measurements beyond total area flow.

scholarly journals Real-Time Burst Detection in District Metering Areas in Water Distribution System Based on Patterns of Water Demand with Supervised Learning

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1765 ◽  
Author(s):  
Pingjie Huang ◽  
Naifu Zhu ◽  
Dibo Hou ◽  
Jinyu Chen ◽  
Yao Xiao ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Water Demand ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Time Window ◽  
Learning Algorithm ◽  
Burst Detection ◽  
Pipe Burst ◽  
Dynamic Time

This paper proposes a new method to detect bursts in District Metering Areas (DMAs) in water distribution systems. The methodology is divided into three steps. Firstly, Dynamic Time Warping was applied to study the similarity of daily water demand, extract different patterns of water demand, and remove abnormal patterns. In the second stage, according to different water demand patterns, a supervised learning algorithm was adopted for burst detection, which established a leakage identification model for each period of time, respectively, using a sliding time window. Finally, the detection process was performed by calculating the abnormal probability of flow during a certain period by the model and identifying whether a burst occurred according to the set threshold. The method was validated on a case study involving a DMA with engineered pipe-burst events. The results obtained demonstrate that the proposed method can effectively detect bursts, with a low false-alarm rate and high accuracy.

scholarly journals Optimal meter placement for pipe burst detection in water distribution systems

2016 ◽  
Vol 18 (4) ◽  
pp. 741-756 ◽  
Author(s):  
Medhanie Hagos ◽  
Donghwi Jung ◽  
Kevin E. Lansey
Keyword(s):  
Pipe Flow ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Roc Curves ◽  
Burst Detection ◽  
Flow Meters ◽  
Meter Placement ◽  
Pipe Burst ◽  
Optimal Meter Placement

Pipe bursts in water distribution systems (WDS) must be rapidly detected to minimize the loss of system functionality and recovery time. Pipe burst is the most common failure in WDS. It results in water loss out of the system, increased head losses, and low pressure at the customers' taps. Therefore, effective and efficient detection of pipe bursts can improve system resilience. To this end, this study proposes an optimal meter placement model to identify meter locations that maximize detection effectiveness for a given number of meters and type of meter. The linear programming model is demonstrated on a modified Austin EPANET hydraulic network. Receiver operating characteristic (ROC) curves for alternative pressure and flow meters are applied to investigate the relationship between the level of available information and pipe burst detection effectiveness. The optimal sensor locations were distinctly different depending on the type of meter and the objective to be considered. The ROC curves for alternative pressure and pipe flow meters showed that pipe flow meters are vulnerable to false alarms, and that using many pipe flow meters could detect all pipe bursts. Pressure meters could detect up to 82% of the burst events.

scholarly journals Study on Pipe Burst Detection Frame Based on Water Distribution Model and Monitoring System

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1363 ◽  
Author(s):  
Weiping Cheng ◽  
Gang Xu ◽  
Hongji Fang ◽  
Dandan Zhao
Keyword(s):  
Distribution System ◽  
Background Noise ◽  
Water Distribution ◽  
Water Distribution Network ◽  
Detection Algorithm ◽  
Distribution Model ◽  
Burst Detection ◽  
Pipe Burst ◽  
Burst Event ◽  
The Impact

This paper describes an infrastructure to detect burst events in a water distribution network, which we illustrate using the Guangzhou water distribution system (WDS). We consider three issues: The feasibility and capability of accurate detection, the layout and design of the monitoring infrastructure, and the burst event detection algorithm. Background noise is identified by analyzing the monitored data. A burst event can be accurately detected only when the impact of the burst can be differentiated from the background noise. We hypothesize that there is a minimum pipe diameter below which accurate burst detection is impossible. We found that data from at least two sensors close to the burst event are required to reduce detection errors.

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