IoT-Based Pipe Burst Detection in Water Distribution Systems

2019 ◽  
pp. 599-604
Author(s):  
Suri Shanmukh ◽  
Meka Poorna Sai ◽  
S. Sai Sri Charan ◽  
Nithya Chidambaram
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Burst Detection ◽  
Pipe Burst

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 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 Non-hydraulic Factors Analysis of Pipe Burst in Water Distribution Systems

2015 ◽  
Vol 119 ◽  
pp. 53-62 ◽  
Author(s):  
Shanshan Li ◽  
Ronghe Wang ◽  
Wenyan Wu ◽  
Jilong Sun ◽  
Yanlong Jing
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Pipe Burst ◽  
Factors Analysis

scholarly journals Near–Real Time Burst Location and Sizing in Water Distribution Systems Using Artificial Neural Networks

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1841
Author(s):  
Miguel Capelo ◽  
Bruno Brentan ◽  
Laura Monteiro ◽  
Dídia Covas
Keyword(s):  
Real Time ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Pressure Head ◽  
Distribution Networks ◽  
Management Tool ◽  
Artificial Data ◽  
Pipe Burst ◽  
Artificial Neural

The current paper proposes a novel methodology for near–real time burst location and sizing in water distribution systems (WDS) by means of Multi–Layer Perceptron (MLP), a class of artificial neural network (ANN). The proposed methodology can be systematized in four steps: (1) construction of the pipe–burst database, (2) problem formulation and ANN architecture definition, (3) ANN training, testing and sensitivity analyses, (4) application based on collected data. A large database needs to be constructed using 24 h pressure–head data collected or numerically generated at different sensor locations during the pipe burst occurrence. The ANN is trained and tested in a real–life network, in Portugal, using artificial data generated by hydraulic extended period simulations. The trained ANN has demonstrated to successfully locate 60–70% of the burst with an accuracy of 100 m and 98% of the burst with an accuracy of 500 m and to determine burst sizes with uncertainties lower than 2 L/s in 90% of tested cases and lower than 0.2 L/s in 70% of the cases. This approach can be used as a daily management tool of water distribution networks (WDN), as long as the ANN is trained with artificial data generated by an accurate and calibrated WDS hydraulic models and/or with reliable pressure–head data collected at different locations of the WDS during the pipe burst occurrence.

scholarly journals How are Various Surrogate Indicators Consistent with Mechanical Reliability of Water Distribution Systems: From a Perspective of Many-Objective Optimization

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1669 ◽  
Author(s):  
Wang ◽  
Huang ◽  
Wang ◽  
Zhan ◽  
Wang ◽  
...  
Keyword(s):  
Optimal Design ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Poor Performance ◽  
Mechanical Reliability ◽  
Design Problems ◽  
Pipe Burst ◽  
The Best Approximation ◽  
The One

Exploring the trade-off between cost and system reliability of water distribution systems (WDSs) has been focused for two decades. Due to the intensive computation associated with the reliability analysis, it is popular in the research community to replace this procedure by using surrogate indicators. However, the discussion on the correlation among different types of such indicators is generally lacking, which implies that a deeper understanding of this aspect is needed. This paper proposes a novel methodology of investigating the relationships among many commonly used surrogate indicators for measuring the mechanical reliability of WDSs. In particular, the optimal design of WDSs is formulated as a many-objective optimization problem, using cost and each surrogate indicator as an individual goal. Two benchmark design problems of different scales and complexities are considered for verifying the proposed method. The well-known multi-objective evolutionary algorithm (MOEA), namely Borg that is suitable for coping with problems involving many objectives, is used to obtain the best approximation to the Pareto-optimal fronts for both cases. Afterward, the one-pipe burst testing is conducted to quantify the correlation between mechanical reliability and surrogate indicators. Results suggest that investigating the correlation of surrogate indicators from the perspective of many-objective optimization provides a direct and efficient way of distinguishing better indicators from worse ones. Resilience-based surrogate indicators and the Redundancy indicator that only depends on nodal pressures are highly related to the mechanical reliability of WDSs. In contrast, entropy-based indicators exhibit poor performance in reflecting the mechanical reliability. These insights contribute to the selection of more appropriate surrogate indicators for the optimal design of WDSs for researchers and practitioners.

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