Efficient Sensor Placement for Leak Localization Considering Uncertainties

Steffelbauer, David; Fuchs-Hanusch, Daniela

doi:10.1007/s11269-016-1504-6

Cited by 51 publications

(41 citation statements)

References 32 publications

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“…Sensor placement in WDN was initially focused on water quality monitoring and it is still an active area of research [31][32][33]. Nevertheless in the last years some sensor placement methodologies for leak localization purposes have been proposed as e.g., in [34], where an entropy-based approach was proposed, in [35], where an efficient branch and bound search is used, in [36][37][38], where Genetic Algorithms are used, in [39], where a prior clustering process is applied and in [40] where the sensor placement was formulated as an hybrid feature selection problem.…”

Section: Sensor Placementmentioning

confidence: 99%

Data-Driven Approach for Leak Localization in Water Distribution Networks Using Pressure Sensors and Spatial Interpolation

Soldevila¹,

Blesa

Fernandez-Canti³

et al. 2019

Water

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This paper presents a new data-driven method for leak localization in water distribution networks. The proposed method relies on the use of available pressure measurements in some selected internal network nodes and on the estimation of the pressure at the remaining nodes using Kriging spatial interpolation. Online leak localization is attained by comparing current pressure values with their reference values. Supported by Kriging; this comparison can be performed for all the network nodes, not only for those equipped with pressure sensors. On the one hand, reference pressure values in all nodes are obtained by applying Kriging to measurement data previously recorded under network operation without leaks. On the other hand, current pressure values at all nodes are obtained by applying Kriging to the current measured pressure values. The node that presents the maximum difference (residual) between current and reference pressure values is proposed as a leaky node candidate. Thereafter, a time horizon computation based on Bayesian reasoning is applied to consider the residual time evolution, resulting in an improved leak localization accuracy. As a data-driven approach, the proposed method does not need a hydraulic model; only historical data from normal operation is required. This is an advantage with respect to most data-driven methods that need historical data for the considered leak scenarios. Since, in practice, the obtained leak localization results will strongly depend on the number of available pressure measurements and their location, an optimal sensor placement procedure is also proposed in the paper. Three different case studies illustrate the performance of the proposed methodologies.

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Section: Sensor Placementmentioning

confidence: 99%

Data-Driven Approach for Leak Localization in Water Distribution Networks Using Pressure Sensors and Spatial Interpolation

Soldevila¹,

Blesa

Fernandez-Canti³

et al. 2019

Water

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show abstract

“…The deployment of sensors needs to consider the characteristics of monitored objects. For sensor deployment of water pipe leak location, in [27], the authors provide a methodology that incorporates uncertainties of different types and sources in the optimal sensor placement problem for leak localization shown by the example of the effect of demand uncertainties on potential pressure measurement points. In [28], for a performance evaluation of ageing infrastructure, Bertola et al presented a measurement system design methodology to identify the best sensor locations and sensor types using information-from static load tests.…”

Section: Related Workmentioning

confidence: 99%

Sparse Sensor Placement for Interpolated Data Reconstruction Based on Iterative Four Subregions in Sensor Networks

et al. 2019

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Data acquisition in large areas has issues of cost and data loss. When sensors are sparse in the physical field, it is critical to study the deployment methods to improve the accuracy of reconstructed data set and the precision of the recovery of lost data. It is desirable to place sensors at optimal locations to achieve higher precision of recovery. In this paper, we present a sparse sensor placement scheme for data interpolation reconstruction based on iterative four subregions using fractal theory. The results of our experiments demonstrate that the precision of our algorithm is higher than that with random placement in dispersion degree, coverage rate, and reconstruction accuracy.

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“…But, the classification accuracy was not improved. A new methodology was introduced in (Steffelbauer and Fuchs-Hanusch, 2016) with uncertainties of different sources in optimal sensor placement issues for leak localization on potential pressure measurement points. The issues were addressed for diverse sensors and uncertainties.…”

Section: Related Workmentioning

confidence: 99%

Hybridisation of brownboost classifier and glowworm swarm based optimal sensor placement for water leakage detection

Rayaroth

Gopalakrishnan

2018

Preprint

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<p><strong>Abstract.</strong> Water Distribution System distributes the water to customer with the better quality and pressure. Distribution system supplies the water from their source to usage point. Due to the leakage, the sufficient amount of water is not delivered to the consumer. Many researchers introduced the techniques for detecting the water leakage in distribution system. But, the water leakage detection accuracy was not improved and time consumption was also not reduced. To improve the water leakage detection performance, Enhanced BrownBoost Classifier based Glowworm Swarm Optimization (EBBC-GWO) Method is introduced. EBBC-GWO method introduces two models namely, Enhanced BrownBoost Classifier model and Glowworm Swarm Optimization model. Enhanced BrownBoost Classifier model considers k-Nearest Neighbor (k-NN) classifier as weak classifier. It classifies the training samples with neighbor's majority vote for allocating the object to the class. Brownboost classifier combines all k-NN classifier to construct strong classifier. By this way, data are classified as the normal data or abnormal data with higher accuracy. After classification, optimization process is executed where every solution corresponds to the glowworm (i.e., abnormal pressure data node) in search space. Every glowworm has objective function for addressing the optimization problem. Every glowworm operates in probabilistic means to choose the neighbor with higher luciferin value and transmit to it. Glowworm updates its location to the glowworm in dynamic decision space and optimal one is selected for water leakage detection. By this way, water leakage detection accuracy is improved with lesser false positive rate. Experimental evaluation of proposed EBBC-GWO method is carried out with respect to number of pressure data and sensor placement nodes. The results demonstrated that EBBC-GWO method is higher in case of classification accuracy, false positive rate, classification time and water leakage detection accuracy. The simulation results show that EBBC-GWO method increases the performance of water leakage detection accuracy and reduces classification time when compared to state-of-the-art works.</p>

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Efficient Sensor Placement for Leak Localization Considering Uncertainties

Cited by 51 publications

References 32 publications

Data-Driven Approach for Leak Localization in Water Distribution Networks Using Pressure Sensors and Spatial Interpolation

Data-Driven Approach for Leak Localization in Water Distribution Networks Using Pressure Sensors and Spatial Interpolation

Sparse Sensor Placement for Interpolated Data Reconstruction Based on Iterative Four Subregions in Sensor Networks

Hybridisation of brownboost classifier and glowworm swarm based optimal sensor placement for water leakage detection

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