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 by
means of data-driven techniques. Specifically several machine learning models will be
developed to perform fault detection. This monitoring includes fault detection and fault
prediction of any of the components of the factory. This project relies upon previous work
done during an internship in the National University of Ireland at Galway in which the
log 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 the
logs of the tri-generation plant and the simulation data obtained for the absorption chiller
subsystem within the tri-generation plant (due to the lack of discriminative information
about faulty behaviour in the real data), and a conceptual data model to describe the
relationships, entities and attributes of that data. The Machine Learning models have
been tested successfully in the absorption chiller subsystem, providing promising results.
