dc.contributor.advisor | Illarramendi Echave, María Aránzazu | |
dc.contributor.advisor | Blanco Arbe, José Miguel | |
dc.contributor.author | Villalobos Rodríguez, Kevin | |
dc.date.accessioned | 2021-02-19T17:03:24Z | |
dc.date.available | 2021-02-19T17:03:24Z | |
dc.date.issued | 2020-07-22 | |
dc.date.submitted | 2020-07-22 | |
dc.identifier.uri | http://hdl.handle.net/10810/50235 | |
dc.description | xvii, 218 p. | es_ES |
dc.description.abstract | 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. | es_ES |
dc.language.iso | eng | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/es/ | * |
dc.subject | artificial intelligence | es_ES |
dc.subject | data banks | es_ES |
dc.subject | informatics | es_ES |
dc.title | Interconnected Services for Time-Series Data Management in Smart Manufacturing Scenarios | es_ES |
dc.type | info:eu-repo/semantics/doctoralThesis | es_ES |
dc.rights.holder | (cc) 2020 Kevin Villaobos Rodríguez (cc by-nc-sa 4.0) | * |
dc.identifier.studentID | 656100 | es_ES |
dc.identifier.projectID | 19408 | es_ES |
dc.departamentoes | Lenguajes y sistemas informáticos | es_ES |
dc.departamentoeu | Hizkuntza eta sistema informatikoak | es_ES |