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Open AccessFeature PaperArticle

A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry

COMAU S.p.A., 10095 Turin, Italy
Laboratory for Manufacturing Systems & Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 265 04 Patras, Greece
Department of Control and Computer engineering, Politecnico di Torino, 10129 Turin, Italy
Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, 10125 Turin, Italy
DELL EMC, P31 D253 Cork, Ireland
Fraunhofer Gesellschaft zur Förderung der angewandten Forschung, 52074 Aachen, Germany
Engineering Ingegneria Informatica S.p.A., 90146 Palermo, Italy
SynArea Consultants S.r.l., 10153 Turin, Italy
Author to whom correspondence should be addressed.
Electronics 2020, 9(3), 492;
Received: 24 December 2019 / Revised: 11 March 2020 / Accepted: 12 March 2020 / Published: 16 March 2020
(This article belongs to the Special Issue Novel Database Systems and Data Mining Algorithms in the Big Data Era)
Data management and processing to enable predictive analytics in cyber physical systems holds the promise of creating insight over underlying processes, discovering anomalous behaviours and predicting imminent failures threatening a normal and smooth production process. In this context, proactive strategies can be adopted, as enabled by predictive analytics. Predictive analytics in turn can make a shift in traditional maintenance approaches to more effective optimising their cost and transforming maintenance from a necessary evil to a strategic business factor. Empowered by the aforementioned points, this paper discusses a novel methodology for remaining useful life (RUL) estimation enabling predictive maintenance of industrial equipment using partial knowledge over its degradation function and the parameters that are affecting it. Moreover, the design and prototype implementation of a plug-n-play end-to-end cloud architecture, supporting predictive maintenance of industrial equipment is presented integrating the aforementioned concept as a service. This is achieved by integrating edge gateways, data stores at both the edge and the cloud, and various applications, such as predictive analytics, visualization and scheduling, integrated as services in the cloud system. The proposed approach has been implemented into a prototype and tested in an industrial use case related to the maintenance of a robotic arm. Obtained results show the effectiveness and the efficiency of the proposed methodology in supporting predictive analytics in the era of Industry 4.0. View Full-Text
Keywords: machine learning; predictive maintenance; visualization techniques; data management; big data architecture machine learning; predictive maintenance; visualization techniques; data management; big data architecture
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Panicucci, S.; Nikolakis, N.; Cerquitelli, T.; Ventura, F.; Proto, S.; Macii, E.; Makris, S.; Bowden, D.; Becker, P.; O’Mahony, N.; Morabito, L.; Napione, C.; Marguglio, A.; Coppo, G.; Andolina, S. A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry. Electronics 2020, 9, 492.

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