Leak diagnosis in pipelines using a combined artificial neural network approach
Leakages in pipelines affect the reliability of fluid transport systems causing environmental damages, economic losses, and pressure reduction at the delivery points. Therefore, this paper presents a methodology to detect and locate water leaks in pipelines by using artificial neural networks (ANN)...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/344802 |
| Acceso en línea: | https://hdl.handle.net/2117/344802 https://dx.doi.org/10.1016/j.conengprac.2020.104677 |
| Access Level: | acceso abierto |
| Palabra clave: | Pipelines Water-supply Neural networks (Computer science) Artificial neural network Water distribution systems Pipelines leak detection Pipeline diagnosis Canonades Aigua -- Abastament Xarxes neuronals (Informàtica) Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
| Sumario: | Leakages in pipelines affect the reliability of fluid transport systems causing environmental damages, economic losses, and pressure reduction at the delivery points. Therefore, this paper presents a methodology to detect and locate water leaks in pipelines by using artificial neural networks (ANN) techniques and online measurements of pressure and flow rate. Contrary to reported works in the literature, the proposed method estimates the friction factor of the pipe and uses this information as an input to compute the leak position. Data generated from a validated numerical simulator was used to enrich the data-training set for the ANN. Various leak scenarios were considered to characterize pressure losses and their differentials in different sections of the pipeline. Finally, the algorithm was tested experimentally in a pilot plant. The results demonstrate good performance and the applicability of the proposed method. |
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