• search hit 31 of 88
Back to Result List

Conception of a Reference Architecture for Machine Learning in the Process Industry

  • The increasing global competition demands continuous optimization of products and processes from companies in the process industry. Where conventional methods of Lean Management and Six Sigma reach their limits, new opportunities and challenges arise through increasing connectivity in the Industrial Internet of Things and machine learning. The majority of industrial projects do not reach the deployment or are isolated solutions, as the structures for data integration, training, deployment and maintenance of models are not established. This paper presents the conception of a reference architecture for machine learning in the process industry to support companies in implementing their own specific structures. The focus is on the development process and an exemplary implementation in the brewing industry.

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Metadaten
Author:Rene Wostmann, Philipp Schlunder, Fabian Temme, Ralf Klinkenberg, Josef Kimberger, Andrea Spichtinger, Markus GoldhackerORCiD, Jochen Deuse
DOI:https://doi.org/10.1109/bigdata50022.2020.9378290
ISBN:978-1-7281-6251-5
Parent Title (English):2020 IEEE International Conference on Big Data (Big Data): 10.12.2020 - 13.12.2020 Atlanta, GA, USA
Publisher:IEEE
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2020
Release Date:2022/02/15
Tag:Big data; Companies; Industrial Internet of Things; Industries; Machine learning; Optimization; Production; Reference architecture; Six sigma; Training; process industry
First Page:1726
Last Page:1735
Institutes:Fakultät Maschinenbau
Begutachtungsstatus:peer-reviewed
research focus:Produktion und Systeme
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG