Pressure Sensor Placement for Leak Localization in Water Distribution Networks Using Information Theory

Santos‐Ruiz, Ildeberto; López‐Estrada, Francisco‐Ronay; Puig, Vicenç; Valencia‐Palomo, Guillermo; León, Héctor Ricardo Hernández-De

doi:10.3390/s22020443

Cited by 22 publications

(11 citation statements)

References 37 publications

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“…In this paper, simulation experiments are performed to select two networks for testing our method. The method is compared with the compressed sensing method in Khorshidi et al (2020) and an information theory method in Santos-Ruiz et al (2022). Three evaluation metrics are used.…”

Section: Evaluation Criteriamentioning

confidence: 99%

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Sensor placement for leak localization in water distribution networks based on graph sampling method

Cai

Wang

2023

Preprint

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The sensor placement is an important research issue for monitoring water distribution networks (WDN). Due to the natural graphical structure of the WDN, it is advantageous to use graph sampling theory for selecting monitoring nodes in the WDN. But the existing graph sampling theory is only applied to processing the band-limited signal, and the pressure data of WDN is a non-band-limited signal. This paper is devoted to solving this problem. The paper proposes an approximate conversion method from non-band-limited signal to band-limited signal and gives an optimal spectrum threshold formula. The threshold formula is applied to perform spectral screening in the graph frequency domain and to convert a non-band-limited signal into a band-limited signal. The band-limited signal retains the major frequency components of the original signal and ignores the frequency components of smaller values. The paper performs graph sampling of the obtained band-limited signal and obtains sampling nodes that can perfectly recover the band-limited signal. The sampling node is used as the monitoring node of the WDN to obtain comprehensive information about the WDN. Our method compares numerically with two methods of optimizing sensor placement. The experiment results demonstrate the effectiveness of the proposed method.

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Section: Evaluation Criteriamentioning

confidence: 99%

“…Some experts have proposed some methods to select sensor nodes based on information entropy. A sensor placement method that maximizes the information relevance of sensor nodes has been proposed (Santos-Ruiz et al 2022). While satisfying the maximum node correlation, the proposed method also requires minimum redundancy between nodes.…”

mentioning

confidence: 99%

Sensor placement for leak localization in water distribution networks based on graph sampling method

Cai

Wang

2023

Preprint

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“…Nonetheless, when building a complete map of the pressure head, two issues arise (a) sensor placement and (b) the estimation of the pressure head at nodes without sensors. Since our paper focuses on estimating pressure heads at nodes without sensors, to overcome the first problem, a well-known sensor placement procedure is used just as described in [2,3], for instance. Thus, we focus on the problem of estimating the pressure head in the nodes without sensors.…”

Section: Introductionmentioning

confidence: 99%

Pressure Interpolation in Water Distribution Networks by Using Gaussian Processes: Application to Leak Diagnosis

Liy-González,

Santos-Ruiz,

Delgado-Aguiñaga

et al. 2024

Processes

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This work presents the reconstruction of the pressure head map of a water distribution system (WDS). This approach relies on historical data collected from a reduced number of sensors placed at some nodes of the WDS. Thus, a Gaussian regression process is then applied to estimate the pressure head at those nodes without a sensor, which allows the reconstruction of the pressure map for the whole network. Then, for leak diagnosis purposes, a dataset of pressure head maps of the WDN is created considering leaky scenarios, and a correlation method is applied to estimate the leak location. Then, for clarity, the Hanoi network is used to evaluate the performance of this leak diagnosis strategy in a simulation environment, assuming the availability of only three sensors. The results showed the potential for pressure head map reconstruction and leak localization.

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“…In the second step, the leak localization strategy is then applied to identify the leak location. This is the core of most leak diagnosis strategies for WDN [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Leak Diagnosis in Water Distribution Systems Based on a Bank of Observers and a Genetic Algorithm

Navarro-Díaz

Delgado-Aguiñaga

Santos‐Ruiz

et al. 2022

Water

Self Cite

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The main contribution of this paper is to present a novel solution for the leak diagnosis problem in branched pipeline systems considering the availability of pressure head and flow rate sensors on the upstream (unobstructed) side and the downstream (constricted) side. This approach is based on a bank of Kalman filters as state observers designed on the basis of the classical water hammer equations and a related genetic algorithm (GA) which includes a fitness function based on an integral error that helps obtaining a good estimation despite the presence of noise. For solving the leak diagnosis problem, three stages are considered: a) the leak detection is performed through a mass balance; b) the region where the leak is occurring is identified by implementing a reduced bank of Kalman filters which localize the leak by sweeping all regions of the branching pipeline through a GA that reduces the computational effort; c) the leak position is computed through an algebraic equation derived from the water hammer equations in steady-state. To assess this methodology, experimental results are presented by using a test bed built at the Tuxtla Gutiérrez Institute of Technology, Tecnológico Nacional de México (TecNM). The obtained results are then compared with those obtained using a classic extended Kalman filter which is widely used in solving leak diagnosis problems and it is highlighted that the GA approach outperforms the EKF in two cases whereas the EKF is better in one case.

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Pressure Sensor Placement for Leak Localization in Water Distribution Networks Using Information Theory

Cited by 22 publications

References 37 publications

Sensor placement for leak localization in water distribution networks based on graph sampling method

Sensor placement for leak localization in water distribution networks based on graph sampling method

Pressure Interpolation in Water Distribution Networks by Using Gaussian Processes: Application to Leak Diagnosis

Real-Time Leak Diagnosis in Water Distribution Systems Based on a Bank of Observers and a Genetic Algorithm

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