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A Machine Learning approach to Predictive Control: study on a real industrial application

Abdeljalil Hajjoubi

A Machine Learning approach to Predictive Control: study on a real industrial application.

Rel. Elena Maria Baralis, Sophie Fosson, Rosaria Rossini. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2018

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Abstract:

The aim of the thesis is to build a system of data collection and analysis, through the integration of a distributed storage and processing system and machine learning techniques, at a small scale. A dataset of industrial data is available for predictive control analysis. Specifically, the dataset contains historical time series of several parameters regarding a manufacturing process, annotated with breakdowns. Our goal is the develop classification strategies that identifies the conditions that cause a breakdown, exploiting Machine Learning tools. Hadoop and TensorFlow, or analogous softwares could be used.

Relators: Elena Maria Baralis, Sophie Fosson, Rosaria Rossini
Academic year: 2017/18
Publication type: Electronic
Number of Pages: 83
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering)
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Aziende collaboratrici: Istituto Superiore Mario Boella
URI: http://webthesis.biblio.polito.it/id/eprint/7572
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