Optimal Gas Leak Localization and Detection using an Autonomous Mobile Robot

Macias, Geronimo; Lee, Kooktae

doi:10.11159/cdsr22.122

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…For instance, the Euler-Lagrange approach has been employed to model pipes conveying water and experiencing leaks under the theory of a rigid column of water. Macias & Kooktae (2022) [7] asserted that it is possible to pinpoint the source of a leak in a gas pipeline using a Hamiltonian approach with the utilization of a backtracking horizon. Similarly, Schneider et al (2002) [8] studied the transient chaos variations when changing the orientation of a major leak within a chaotic region in closed Hamiltonian systems.…”

Section: Introductionmentioning

confidence: 99%

Development of a Conservative Hamiltonian Dynamic System for the Early Detection of Leaks in Pressurized Pipelines

Ladino-Moreno,

García-Ubaque,

Zamudio-Huertas

2024

Civ Eng J

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In this study, we propose an innovative approach for real-time leakage detection in pipelines by integrating conservative Hamiltonian equations and experimental Internet of Things (IoT) technologies. The proposed method combines a hybrid model that utilizes sensors and IoT devices to acquire real-time data and solves the coupled system of Hamiltonian equations using the ODE45 numerical integration method. Spectral frequency analysis is an essential part of this method, as it reveals specific patterns in the pressure and flow signals. The findings highlighted 95% accuracy in leak detection, which was validated through a comparison of the theoretical and experimental data. The novelty of this approach lies in its ability to maintain constant total system energy, thereby enabling continuous monitoring for early leak detection. As an improvement, the proper handling of sensor signals is emphasized, underscoring its contribution to the efficient management of water resources in potable water distribution systems. Doi: 10.28991/CEJ-2024-010-04-01 Full Text: PDF

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Section: Introductionmentioning

confidence: 99%