RT info:eu-repo/semantics/masterThesis
T1 Health monitoring of a TriGen plant: a Big Data proposal
A1 Arias Requejo, Desirée
A2 Universidad de Valladolid. Escuela de Ingeniería Informática de Valladolid
K1 Health monitoring
K1 TriGen plant
K1 Big Data proposal
AB Nowadays, energy efficiency is becoming a critical factor in factories all over the world.Thanks to proper and timely monitoring of the operation and performance of the factories,remarkable energy savings can be obtained.This project aims to perform health monitoring in large factories or corporations bymeans of data-driven techniques. Specifically several machine learning models will bedeveloped to perform fault detection. This monitoring includes fault detection and faultprediction of any of the components of the factory. This project relies upon previous workdone during an internship in the National University of Ireland at Galway in which thelog files of the Boston Scientific Corporation's (BSC) tri-generation plant were studied.This work contains a Big Data architecture's proposal to store all the data from both thelogs of the tri-generation plant and the simulation data obtained for the absorption chillersubsystem within the tri-generation plant (due to the lack of discriminative informationabout faulty behaviour in the real data), and a conceptual data model to describe therelationships, entities and attributes of that data. The Machine Learning models havebeen tested successfully in the absorption chiller subsystem, providing promising results.
YR 2018
FD 2018
LK http://uvadoc.uva.es/handle/10324/33362
UL http://uvadoc.uva.es/handle/10324/33362
LA eng
NO Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)
DS UVaDOC
RD 24-abr-2024