Leak detection and localization in water distribution networks: Review and perspective

Romero-Ben, Luis; Alves, Débora; Blesa, Joaquim; Cembraño, Gabriela; Puig, Vicenç; Duviella, E.

doi:10.1016/j.arcontrol.2023.03.012

Cited by 26 publications

(4 citation statements)

References 128 publications

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“…For decades, numerous leak detection methods have been developed, most of which identify leaks based on measured eld data (e.g., pipe ow and pressure) and allow to establish software-based approaches (Wan et al 2022;Romero-Ben et al 2023). These tools can be divided into classi cation, residual-based, and system mass-balancing approaches ).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Impact of Water Distribution Network Characteristics on Detectable Leak Sizes

Jun,

Jung

2023

Preprint

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Various leak detection methods have been developed for water distribution networks (WDNs). Since sufficient tools are already available, creating further models draws less attention over time. Instead, existing methods should be evaluated across various WDNs to examine the impact of network characteristics on the detectable leak size (QDL). This study tests a convolutional neural network-based leak detection model for 12 WDNs. Each network has different characteristics such as system demand, pipe diameter and length, and topology. Seven leak sizes are evaluated per network, and two detection performance metrics, namely, the detection probability (DP) and rate of false alarms (RF), are calculated to assess QDL. Additionally, a new metric is derived to simultaneously evaluate DP and RF. The results indicate that QDL varies substantially depending on the system type, either transmission-oriented or distribution-oriented. Identifying leaks for the former system is challenging, whereas the latter exhibits high DPs with acceptable number of false alarms when detecting small leaks (e.g., 1 L/s). Moreover, QDL is more sensitive to hydraulics than topological characteristics (e.g., branch index). The combination of network parameters in the energy loss equation provided the most suitable relationship with leak detection.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Impact of Water Distribution Network Characteristics on Detectable Leak Sizes

Jun,

Jung

2023

Preprint

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“…Changes in the flow of individual pipes are observed in order to identify leaks in a WDN, and particularly in pipes connected to the reservoir. Current flow centrality is effective for identifying larger leaks, but it is less effective for smaller ones, as these generate less noticeable signatures [23] . Hence, to accurately locate small leaks, it is necessary to analyze more detailed data, such as the flow changes in the surrounding pipes affected by the leak.…”

Section: Literature Reviewmentioning

confidence: 99%

GA-Sense: Sensor placement strategy for detecting leaks in water distribution networks based on time series flow and genetic algorithm

Shiddiqi,

Za'in,

Lathifah

et al. 2024

MethodsX

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“…Pipelines are crucial elements in many engineering systems and are widely used to transport water [1,2]. However, the efficiency of these systems can be compromised by issues such as water leaks [3,4]. When undetected or neglected, these leaks can lead to significant wastage of water, posing both environmental and economic challenges across the world [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An Investigation into the Physical Mechanisms of Leak Noise Propagation in Buried Plastic Water Pipes: A Wave Dynamic Stiffness Approach

Scussel,

Brennan,

Muggleton

et al. 2024

Acoustics

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In buried plastic water pipes, the predominantly fluid-borne wave is of particular interest, as it plays a key role in the propagation of leak noise. Consequently, it has been studied by several researchers to determine the speed of wave propagation and its attenuation with distance. These features are encapsulated in the wavenumber. By examining the factors that govern the behaviour of this wavenumber, this paper presents an in-depth examination of the physical mechanisms of leak noise propagation. To achieve this, an alternative physics-based model for the wavenumber is developed, using the concept of the wave dynamic stiffnesses of the individual components within the pipe system, i.e., the water in the pipe, the pipe wall, and the surrounding medium. This facilitates a clear interpretation of the wave behaviour in terms of the physical properties of the system, especially the interface between the pipe and the surrounding medium, which can have a profound influence on the leakage of acoustic energy from the pipe wall into the external medium. Three systems with different types of surrounding medium are studied, and the factors that govern leak noise propagation in each case are identified. Experimental results on two distinct test sites from different parts of the world are provided to validate the approach using leak noise as an excitation mechanism.

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Leak detection and localization in water distribution networks: Review and perspective

Cited by 26 publications

References 128 publications

RETRACTED: Impact of Water Distribution Network Characteristics on Detectable Leak Sizes

RETRACTED: Impact of Water Distribution Network Characteristics on Detectable Leak Sizes

GA-Sense: Sensor placement strategy for detecting leaks in water distribution networks based on time series flow and genetic algorithm

An Investigation into the Physical Mechanisms of Leak Noise Propagation in Buried Plastic Water Pipes: A Wave Dynamic Stiffness Approach

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