A review of methods for burst/leakage detection and location in water distribution systems

The problem of bursts and leakages in water distribution systems has received significantly increased attention over the past two decades. As they represent both an environmental and an economical issue, how to reduce water loss through bursts and leakages is a challenging task for water utilities....

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Bibliographic Details
Published in:Water science & technology. Water supply 2015-01, Vol.15 (3), p.429-441
Main Authors: Li, Rui, Huang, Haidong, Xin, Kunlun, Tao, Tao
Format: Article
Language:English
Subjects:
Acoustic noise
Acoustics
Analysis
Artificial intelligence
Artificial neural networks
Bayesian analysis
Bursting
Bursts
Categories
Civil engineering
Computer programs
Correlators
Data analysis
Data processing
Distribution
Frequency domain analysis
Gas injection
Ground penetrating radar
Hardware
Hydraulics
Infrared photography
Infrared radar
Kalman filters
Leak detection
Leaks
Listening
Mathematical models
Methods
Modelling
Neural networks
Noise
Photography
Pipelines
Probability theory
Radar
Rods
Sensors
Software
Sonar detection
Sound
Sound detecting and ranging
Statistical analysis
Systems analysis
Time domain analysis
Water distribution
Water distribution systems
Water engineering
Water loss
Water utilities
Wavelet transforms
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Summary:The problem of bursts and leakages in water distribution systems has received significantly increased attention over the past two decades. As they represent both an environmental and an economical issue, how to reduce water loss through bursts and leakages is a challenging task for water utilities. Consequently, various techniques have been developed to detect the location and size of leakages. The methods for bursts (or leaks) detection and location can be broadly divided into two main categories, one based on hardware and the other based on software. Hardware-based methods include (i) acoustic detection methods such as listening rods, leak correlators, leak noise loggers and (ii) non-acoustic detection methods such as gas injection, ground penetrating radar technology and infrared photography. Software-based methods make use of the data collected by real-time pressure and/or flow sensors and several artificial intelligence techniques and statistical data analysis tools, including (i) methods based on numerical modeling methods, such as inverse transient analysis, time domain analysis and frequency domain analysis, and (ii) some non-numerical modeling methods, such as artificial neural networks, Bayesian inference systems, the Golden section method, and Kalman filtering. In this article, the authors describe the methods for pipe network burst location and detection, summarize the features of each method, and propose a suggestion for future work.
ISSN:1606-9749
1607-0798
DOI:10.2166/ws.2014.131