2019
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Al-Gumaei, K; Müller, A; Weskamp, J N; Longo, C S; Florian, P; Windmann, S Scalable Analytics Platform for Machine Learning in Smart Production Systems Conference IEEE, 2019. Abstract | Links | BibTeX | Tags: conference @conference{10.1109/ETFA.2019.8869075,
title = {Scalable Analytics Platform for Machine Learning in Smart Production Systems},
author = {Al-Gumaei,K and Müller, A and Weskamp, J. N. and Longo, C.S. and Florian, P. and Windmann, S.},
url = {https://ieeexplore.ieee.org/document/8869075/authors#authors},
doi = {10.1109/ETFA.2019.8869075},
year = {2019},
date = {2019-10-17},
publisher = {IEEE},
abstract = {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.},
keywords = {conference},
pubstate = {published},
tppubtype = {conference}
}
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. |
2018
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Longo, C S; Fantuzzi, C; Monica, F; Manfredotti, L; Sorge, M Big Data for advanced monitoring system: an approach to manage system complexity Conference 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 2018, ISBN: 978-1-5386-3593-3. Abstract | Links | BibTeX | Tags: @conference{Longo2018,
title = {Big Data for advanced monitoring system: an approach to manage system complexity},
author = {Longo, C.S. and Fantuzzi, C. and Monica, F. and Manfredotti, L. and Sorge, M.},
doi = {10.1109/COASE.2018.8560552},
isbn = {978-1-5386-3593-3},
year = {2018},
date = {2018-12-06},
booktitle = {2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)},
abstract = {Big Data is the nowadays world current trend. Millions of sensors are connected to the everyday life devices, ingesting petabytes of data per day and helping companies improve their products. This paper provides an example of connecting the automation world with the Big Data world: sensor data streamed by real world operating machines is stored in databases, analysed and displayed in real-time dashboards with the intent of tracking the machines operating status and alerting the technicians in case maintenance is needed.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Big Data is the nowadays world current trend. Millions of sensors are connected to the everyday life devices, ingesting petabytes of data per day and helping companies improve their products. This paper provides an example of connecting the automation world with the Big Data world: sensor data streamed by real world operating machines is stored in databases, analysed and displayed in real-time dashboards with the intent of tracking the machines operating status and alerting the technicians in case maintenance is needed. |
Longo, C S; Fantuzzi, C Simulation and optimization of industrial production lines Journal Article at-Automatisierungstechnik, 66 (4), pp. 320–330, 2018. BibTeX | Tags: @article{santo2018simulation,
title = {Simulation and optimization of industrial production lines},
author = {Longo, C.S. and Fantuzzi, C. },
year = {2018},
date = {2018-01-01},
journal = {at-Automatisierungstechnik},
volume = {66},
number = {4},
pages = {320--330},
publisher = {De Gruyter Oldenbourg},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2017
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Longo, C S; Fantuzzi, C Simulation and optimisation of production lines in the framework of the IMPROVE project Conference 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2017. Links | BibTeX | Tags: @conference{8247582,
title = {Simulation and optimisation of production lines in the framework of the IMPROVE project},
author = {Longo, C.S. and Fantuzzi, C.},
doi = {10.1109/ETFA.2017.8247582},
year = {2017},
date = {2017-09-01},
booktitle = {2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)},
pages = {1-8},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
