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Scalable Analytics Platform for Machine Learning in Smart Production Systems

: Al-Gumaei, Khaled; Müller, Arthur; Weskamp, Jan Nicolas; Longo, Claudio Santo; Pethig, Florian; Windmann, Stefan


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019. Proceedings : Zaragoza, Spain, 10 - 13 September 2019
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-0303-7
ISBN: 978-1-7281-0302-0
ISBN: 978-1-7281-0304-4
International Conference on Emerging Technologies and Factory Automation (ETFA) <24, 2019, Zaragoza>
Fraunhofer IOSB ()
Big Data; machine learning; Industrie 4.0; Industrial automation

Manufacturing industry is facing major challenges to meet customer requirements, which are constantly changing. Therefore, products have to be manufactured with efficient processes, minimal interruptions, and low resource consumptions. To achieve this goal, huge amounts of data generated by industrial equipment needs to be managed and analyzed by modern technologies. Since the big data era in manufacturing industry is still at an early stage, there is a need for a reference architecture that incorporates big data and machine learning technologies and aligns with the Industrie 4.0 standards and requirements. In this paper, requirements for designing a scalable analytics platform for industrial data are derived from Industrie 4.0 standards and literature. Based on these requirements, a reference big data architecture for industrial machine learning applications is proposed and compared to related works. Finally, the proposed architecture has been implemented in the Lab Big Data at the SmartFactoryOWL and its scalability and performance have been evaluated on parallel computation of an industrial PCA model. The results show that the proposed architecture is linearly scalable and adaptable to machine learning use cases and will help to improve the industrial automation processes in production systems.