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Title: Implementación en Azure Cloud de un sistema simulado de eficiencia energética, prevención de fallos de calidad y virtualización de sensores para una industria de laminación de tubos en caliente
Author: Manzano Lejarza, Julen
Director: Isern Alarcón, David
Tutor: Ventura Royo, Carles  
Others: Universitat Oberta de Catalunya
Keywords: real time
machine learning
deep learning
Microsoft Azure
Issue Date: Jun-2018
Publisher: Universitat Oberta de Catalunya
Abstract: The artificial intelligence systems in the industrial sector are being supported by the entry of serverless services in the cloud, allowing distributed computing to a degree of scalability which was impossible before, as well as the deployment and development of proyects that allow the abstraction of issues related to deployment, scalability, maintenance and securiy. By using these architectures, in this case Microsoft's Azure, we can create advanced analytic models for the designation of virtual sensors, detection of energetic anomalies and the overseeing of a qualified system that are put into a process of information management in real time that allows us, after going through the behaviour models obtained, to get an information output crucial for the proper operation of industrial systems, and what is more important, able to add relevant information to our factory in the same moment it is created, allowing its use by an automatized decision-making system using thesame information architecture, acting on the systems optimizing their configuration at every moment. This information is offered in a control panel or business intelligence that shows us not only the gathered and processed data in real time, but also a history of information that will allow the expert to take strategic decisions based on information and experience.
Language: Spanish
URI: http://hdl.handle.net/10609/81488
Appears in Collections:Bachelor thesis, research projects, etc.

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Presentacion TFG Julen Manzano_Inteligencia_Artificial_reducido.mp4Video de presentación30.13 MBMP4View/Open
Video_sistema.mp4Vídeo funcionamiento del sistema22.9 MBMP4View/Open
jmanzano130TFG0618memoria.pdfMemoria del TFG3.95 MBAdobe PDFView/Open
jmanzano130TFG0618presentación.pdfPresentación del TFG8.52 MBAdobe PDFView/Open

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