Aplicación de técnicas de machine Learning para la detección de fugas en una tubería horizontal que transporta una mezcla de agua y glicerol
During the last decades, the problem of detecting leaks in pipes with the help of software has been much discussed. Timely leak detection prevents water loss and helps prevent economic and environmental catastrophes. It is evident that companies choose to have good leak control policies, detection s...
- Autores:
-
Gámez De León, Adalberto
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8874
- Acceso en línea:
- https://hdl.handle.net/11323/8874
https://repositorio.cuc.edu.co/
- Palabra clave:
- Machine learning
Leak detection
Decision trees
Support Vector Machine SVM
Artificial intelligence
Detección de fugas
Árboles de decisión
Máquinas de Soporte Vectorial SVM
Inteligencia artificial
- Rights
- openAccess
- License
- Attribution-NonCommercial-ShareAlike 4.0 International
| Summary: | During the last decades, the problem of detecting leaks in pipes with the help of software has been much discussed. Timely leak detection prevents water loss and helps prevent economic and environmental catastrophes. It is evident that companies choose to have good leak control policies, detection systems in Control Centers and teams of operators who locate leaks directly on the ground. However, these strategies do not allow for real-time leak detection. This project implements techniques based on machine learning that, through the introduction of process data from the studied pipes, systematically determine the presence of leaks. With this project, it is hoped to improve the speed and accuracy of leak detection, using only process data, system knowledge and intelligent software. |
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