Benchmarking dataset for leak detection and localization in water distribution systems

Mohsen Aghashahi; Lina Sela; M. Katherine Banks

doi:10.1016/j.dib.2023.109148

Benchmarking dataset for leak detection and localization in water distribution systems

Mohsen Aghashahi, Lina Sela, M. Katherine Banks

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

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.

Original language	English (US)
Article number	109148
Pages (from-to)	109148
Journal	Data in Brief
Volume	48
DOIs	https://doi.org/10.1016/j.dib.2023.109148
State	Published - Jun 2023

Keywords

Anomaly detection
Leak
Sensors
Supervised and unsupervised classification
Water networks

ASJC Scopus subject areas

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10.1016/j.dib.2023.109148

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title = "Benchmarking dataset for leak detection and localization in water distribution systems",

abstract = "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.",

keywords = "Anomaly detection, Leak, Sensors, Supervised and unsupervised classification, Water networks",

author = "Mohsen Aghashahi and Lina Sela and Banks, {M. Katherine}",

note = "Funding Information: Funding: The testbed construction was made possible in part by the Texas Sea Grant Grants-in-Aid of Graduate Research Program 2019–2021 under the grant NA18–2019-E/GIA-2019-Aghash and this work was also supported in part by the National Science Foundation under Grant 1943428 . We would like to thank Dr. Jayson Barfknecht from Bryan Water Services Department of the City of Bryan, Texas, for providing the industrial expertise and knowledge during the development of the testbed. Publisher Copyright: {\textcopyright} 2023 The Author(s)",

year = "2023",

month = jun,

doi = "10.1016/j.dib.2023.109148",

language = "English (US)",

volume = "48",

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AU - Sela, Lina

AU - Banks, M. Katherine

N1 - Funding Information: Funding: The testbed construction was made possible in part by the Texas Sea Grant Grants-in-Aid of Graduate Research Program 2019–2021 under the grant NA18–2019-E/GIA-2019-Aghash and this work was also supported in part by the National Science Foundation under Grant 1943428 . We would like to thank Dr. Jayson Barfknecht from Bryan Water Services Department of the City of Bryan, Texas, for providing the industrial expertise and knowledge during the development of the testbed. Publisher Copyright: © 2023 The Author(s)

PY - 2023/6

Y1 - 2023/6

N2 - 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.

AB - 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.

KW - Anomaly detection

KW - Leak

KW - Sensors

KW - Supervised and unsupervised classification

KW - Water networks

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Benchmarking dataset for leak detection and localization in water distribution systems

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