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2020 | OriginalPaper | Chapter

Machine Learning for Predictive and Prescriptive Analytics of Operational Data in Smart Manufacturing

Authors : Katerina Lepenioti, Minas Pertselakis, Alexandros Bousdekis, Andreas Louca, Fenareti Lampathaki, Dimitris Apostolou, Gregoris Mentzas, Stathis Anastasiou

Published in: Advanced Information Systems Engineering Workshops

Publisher: Springer International Publishing

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Abstract

Perceiving information and extracting insights from data is one of the major challenges in smart manufacturing. Real-time data analytics face several challenges in real-life scenarios, while there is a huge treasure of legacy, enterprise and operational data remaining untouched. The current paper exploits the recent advancements of (deep) machine learning for performing predictive and prescriptive analytics on the basis of enterprise and operational data aiming at supporting the operator on the shopfloor. To do this, it implements algorithms, such as Recurrent Neural Networks for predictive analytics, and Multi-Objective Reinforcement Learning for prescriptive analytics. The proposed approach is demonstrated in a predictive maintenance scenario in steel industry.

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next chapter Towards Predictive Maintenance for Flexible Manufacturing Using FIWARE
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Metadata
Title
Machine Learning for Predictive and Prescriptive Analytics of Operational Data in Smart Manufacturing
Authors
Katerina Lepenioti
Minas Pertselakis
Alexandros Bousdekis
Andreas Louca
Fenareti Lampathaki
Dimitris Apostolou
Gregoris Mentzas
Stathis Anastasiou
Copyright Year
2020
Book
Advanced Information Systems Engineering Workshops

Print ISBN: 978-3-030-49164-2

Electronic ISBN: 978-3-030-49165-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-49165-9_1

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