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Benchmarking dataset for leak detection and localization in water distribution systems

This paper presents a dataset with two hundred and eighty sensory measurements for leak detection and localization in water distribution systems. The data were generated via a laboratory-scale water distribution system that included (1) three types of sensors: accelerometer, hydrophone, and dynamic...

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Detalles Bibliográficos
Autores principales: Aghashahi, Mohsen, Sela, Lina, Banks, M. Katherine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147960/
https://www.ncbi.nlm.nih.gov/pubmed/37128586
http://dx.doi.org/10.1016/j.dib.2023.109148
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author Aghashahi, Mohsen
Sela, Lina
Banks, M. Katherine
author_facet Aghashahi, Mohsen
Sela, Lina
Banks, M. Katherine
author_sort Aghashahi, Mohsen
collection PubMed
description This paper presents a dataset with two hundred and eighty sensory measurements for leak detection and localization in water distribution systems. The data were generated via a laboratory-scale water distribution system that included (1) three types of sensors: accelerometer, hydrophone, and dynamic pressure sensor; (2) four leak types: orifice leak, longitudinal and circumferential cracks, gasket leak, and no-leak condition; (3) two network topologies: looped and branched; and (4) six background conditions with different noise and demand variations. Each measurement was 30 s long, and the measurement frequencies were 51.2 kHz for the accelerometer and dynamic pressure sensors, and 8 kHz for the hydrophone. This is the first publicly available dataset for advancing leak detection and localization research, model validation, and generating new data for faulty sensor detection in water distribution systems.
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spelling pubmed-101479602023-04-30 Benchmarking dataset for leak detection and localization in water distribution systems Aghashahi, Mohsen Sela, Lina Banks, M. Katherine Data Brief Data Article This paper presents a dataset with two hundred and eighty sensory measurements for leak detection and localization in water distribution systems. The data were generated via a laboratory-scale water distribution system that included (1) three types of sensors: accelerometer, hydrophone, and dynamic pressure sensor; (2) four leak types: orifice leak, longitudinal and circumferential cracks, gasket leak, and no-leak condition; (3) two network topologies: looped and branched; and (4) six background conditions with different noise and demand variations. Each measurement was 30 s long, and the measurement frequencies were 51.2 kHz for the accelerometer and dynamic pressure sensors, and 8 kHz for the hydrophone. This is the first publicly available dataset for advancing leak detection and localization research, model validation, and generating new data for faulty sensor detection in water distribution systems. Elsevier 2023-04-14 /pmc/articles/PMC10147960/ /pubmed/37128586 http://dx.doi.org/10.1016/j.dib.2023.109148 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Aghashahi, Mohsen
Sela, Lina
Banks, M. Katherine
Benchmarking dataset for leak detection and localization in water distribution systems
title Benchmarking dataset for leak detection and localization in water distribution systems
title_full Benchmarking dataset for leak detection and localization in water distribution systems
title_fullStr Benchmarking dataset for leak detection and localization in water distribution systems
title_full_unstemmed Benchmarking dataset for leak detection and localization in water distribution systems
title_short Benchmarking dataset for leak detection and localization in water distribution systems
title_sort benchmarking dataset for leak detection and localization in water distribution systems
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147960/
https://www.ncbi.nlm.nih.gov/pubmed/37128586
http://dx.doi.org/10.1016/j.dib.2023.109148
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