The leak signal characteristics and estimation of the leak location on water pipeline

Park, Sang-Bong; Kim, Ki-Bum; Seo, Jeewon; Kim, Jueon; Koo, Ja−Yong

doi:10.11001/jksww.2018.32.5.461

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…Another field method utilizes a leakage sound detector with a listening stick, an electronic leakage detector, and a noise logger to locate the leakage in the pipeline accurately. Research has been conducted to specify the signals from leakages precisely [3]. However, for leakage detection in the field, the availability and recognition of the leakage is prioritized and leakage identification by reporting is the main task.…”

Section: Introductionmentioning

confidence: 99%

The Development of a Data-Based Leakage Pinpoint Detection Technique for Water Distribution Systems

Kim

Choi

2023

Mathematics

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Leakage is one of the abnormal conditions in water distribution systems (WDSs). Real-time monitoring can be used to prevent or recover quickly from leakage. However, this is not enough: for improved leakage detection, a status diagnosis of the WDS must be performed together with this real-time monitoring, and numerous studies have been conducted on this. Furthermore, the existing proposed methodology only provides optimal sensor location and fast recognition. This paper proposes a technique that can quantitatively evaluate the volume of leakage along with leakage detection using deep learning technology. The hydraulic data (e.g., pressure, velocity, and flow) from the calibrated hydraulic model were used as training data and deep learning techniques were applied to conduct a simultaneous detection of leakage volume and location. We examined various scenarios regarding leakage volume and location for the data configuration of a simulated leakage accident. Furthermore, for optimal leakage detection performance, the detection performance according to the size of the network, the meter types of meters, the number of meters, and the locations of the meters were analyzed. This study is expected to be helpful in various aspects such as recovery and restoration decision making after leakage, because it simultaneously identifies the amount and location of the leakage.

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Section: Introductionmentioning

confidence: 99%

The Development of a Data-Based Leakage Pinpoint Detection Technique for Water Distribution Systems

Kim

Choi

2023

Mathematics

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Development of a Machine-learning-based Framework to Assess the Performance of Water Distribution Systems

Choi,

Kim,

Kim

2024

J. Korean Soc. Hazard Mitig

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Recent statistics indicate that the national water supply coverage is approximately 99%, reflecting a high level of service. However, the aging infrastructure continues to cause issues such as pipe failures and water quality concerns, creating operational difficulties. To solve these issues, performance assessments are used to quantitatively evaluate the system conditions and ensure effective maintenance. In Korea, these assessments are based on a scoring system that combines direct and indirect assessments. However, direct assessment of all pipes is limited by cost and time constraints. To address this issue, a deep neural network is used to assess the aging of water distribution systems. This study develops a framework to estimate direct results from indirect data by analyzing the correlation between indirect and direct assessments. Additionally, data augmentation is considered to compensate for the lack of training data in direct assessments, improve the reliability of performance assessments, and support better prioritization in system maintenance.

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The leak signal characteristics and estimation of the leak location on water pipeline

Cited by 2 publications

References 5 publications

The Development of a Data-Based Leakage Pinpoint Detection Technique for Water Distribution Systems

The Development of a Data-Based Leakage Pinpoint Detection Technique for Water Distribution Systems

Development of a Machine-learning-based Framework to Assess the Performance of Water Distribution Systems

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