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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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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. |
format | Online Article Text |
id | pubmed-10147960 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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