Services to Facilitate Predictive Maintenance in Industry4.0-Reference-Cited by-同舟云学术

Services to Facilitate Predictive Maintenance in Industry4.0

Published:2021 Issue: Volume: Page:75-95
ISSN:2510-1528
Container-title:Information Fusion and Data Science
language:
Short-container-title:

Author:

Nikolakis Nikolaos,Cerquitelli Tania,Siaterlis Georgios,Bellagarda Andrea,Macii Enrico,Coppo Guido,Andolina Salvatore,Bartholomauss Florian,Plutz Martin,Alexopoulos Kosmas

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-2940-2_4

Reference17 articles.

1. T. Cerquitelli, D.J. Pagliari, A. Calimera, L. Bottaccioli, E. Patti, A. Acquaviva, M. Poncino, Manufacturing as a data-driven practice: methodologies, technologies, and tools, in Proceedings of the IEEE (2021)

2. N. Nikolakis, A. Marguglio, G. Veneziano, P. Greco, S. Panicucci, T. Cerquitelli, E. Macii, S. Andolina, K. Alexopoulos, A microservice architecture for predictive analytics in manufacturing. Proc. Manuf. 51, 1091 (2020). https://doi.org/10.1016/j.promfg.2020.10.153

3. S. Panicucci, N. Nikolakis, T. Cerquitelli, F. Ventura, S. Proto, E. Macii, S. Makris, D. Bowden, P. Becker, N. O’mahony, L. Morabito, C. Napione, A. Marguglio, G. Coppo, S. Andolina, A cloud-to-edge approach to support predictive analytics in robotics industry. Electronics (Switzerland) 9(3) (2020). https://doi.org/10.3390/electronics9030492

4. S. Proto, F. Ventura, D. Apiletti, T. Cerquitelli, E. Baralis, E. Macii, A. Macii, Premises, a scalable data-driven service to predict alarms in slowly-degrading multi-cycle industrial processes, in 2019 IEEE International Congress on Big Data, BigData Congress 2019, Milan, Italy, July 8-13, 2019, ed. by E. Bertino, C.K. Chang, P. Chen, E. Damiani, M. Goul, K. Oyama (IEEE, 2019), pp. 139–143

5. F. Ventura, S. Proto, D. Apiletti, T. Cerquitelli, S. Panicucci, E. Baralis, E. Macii, A. Macii, A new unsupervised predictive-model self-assessment approach that SCALEs, in Proceedings—2019 IEEE International Congress on Big Data, BigData Congress 2019—Part of the 2019 IEEE World Congress on Services pp. 144–148 (2019). https://doi.org/10.1109/BigDataCongress.2019.00033