Interconnected services for time-series data management in smart manufacturing scenarios
- Autores: Kevin Villalobos Rodriguez
- Directores de la Tesis: José Miguel Blanco Arbe (dir. tes.)
, Arantza Illarramendi Echave (dir. tes.) - Lectura: En la Universidad del País Vasco - Euskal Herriko Unibertsitatea ( España ) en 2020
- Idioma: inglés
- Tribunal Calificador de la Tesis: Nieves R. Brisaboa (presid.) , Alfredo Goñi Sarriguren (secret.) , Philippe Roose (voc.)
- Enlaces
- Tesis en acceso abierto en: ADDI
- Resumen
The rise of Smart Manufacturing, together with the strategic initiatives carried out worldwide, have promoted its adoption among manufacturers who are increasingly interested in boosting data-driven applications for different purposes, such as product quality control, predictive maintenance of equipment, etc. However, the adoption of these approaches faces diverse technological challenges with regard to the data-related technologies supporting the manufacturing data life-cycle. The main contributions of this dissertation focus on two specific challenges related to the early stages of the manufacturing data life-cycle: an optimized storage of the massive amounts of data captured during the production processes and an efficient pre-processing of them. The first contribution consists in the design and development of a system that facilitates the pre-processing task of the captured time-series data through an automatized approach that helps in the selection of the most adequate pre-processing techniques to apply to each data type. The second contribution is the design and development of a three-level hierarchical architecture for time-series data storage on cloud environments that helps to manage and reduce the required data storage resources (and consequently its associated costs). Moreover, with regard to the later stages, a thirdcontribution is proposed, that leverages advanced data analytics to build an alarm prediction system that allows to conduct a predictive maintenance of equipment by anticipating the activation of different types of alarms that can be produced on a real Smart Manufacturing scenario.
