Technological advances in areas such as communications, computer processing, connectivity, data management are gradually introducing the internet of things (IoT) paradigm across companies of different domain. In this context and as systems are making a shift into cyberphysical system of systems, connected devices provide massive data, that are usually streamed to a central node for further processing. In particular and related to the manufacturing domain, data processing can provide insight in the operational condition of the organization or process monitored. However, there are near real time constraints for such insights to be generated and data-driven decision making to be enabled. In the context of internet of things for smart manufacturing and empowered by the aforementioned, this study discusses a fog computing paradigm for enabling maintenance related predictive analytic in a manufacturing environment through a two step approach: 1. Model training on the cloud, 2) Model execution on the edge. The proposed approach has been applied to a use case coming from the robotic industry.

A fog computing approach for predictive maintenance / Cerquitelli, T.; Bowden, D.; Marguglio, A.; Morabito, L.; Napione, C.; Panicucci, S.; Nikolakis, N.; Makris, S.; Coppo, G.; Andolina, S.; Macii, A.; Macii, E.; O'Mahony, N.; Becker, P.; Jung, S.. - STAMPA. - 349:(2019), pp. 139-147. ((Intervento presentato al convegno 31st International Conference on Advanced Information Systems Engineering, CAiSE 2019 - Workshops tenutosi a Rome (italy) nel June 3-7, 2019, [10.1007/978-3-030-20948-3_13].

A fog computing approach for predictive maintenance

Cerquitelli T.;Macii A.;Macii E.;
2019

Abstract

Technological advances in areas such as communications, computer processing, connectivity, data management are gradually introducing the internet of things (IoT) paradigm across companies of different domain. In this context and as systems are making a shift into cyberphysical system of systems, connected devices provide massive data, that are usually streamed to a central node for further processing. In particular and related to the manufacturing domain, data processing can provide insight in the operational condition of the organization or process monitored. However, there are near real time constraints for such insights to be generated and data-driven decision making to be enabled. In the context of internet of things for smart manufacturing and empowered by the aforementioned, this study discusses a fog computing paradigm for enabling maintenance related predictive analytic in a manufacturing environment through a two step approach: 1. Model training on the cloud, 2) Model execution on the edge. The proposed approach has been applied to a use case coming from the robotic industry.
978-3-030-20947-6
978-3-030-20948-3
File in questo prodotto:
File Dimensione Formato  
CAISE2019 - A fog coputing approach for predictive maintenance.pdf

accesso aperto

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 1.21 MB
Formato Adobe PDF
1.21 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2786772