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Supporting Predictive Maintenance in Virtual Factory.

Sang, G. M., Xu, L. and de Vrieze, P. T., 2021. Supporting Predictive Maintenance in Virtual Factory. In: PRO-VE 2021, Smart and Sustainable Collaborative Networks 4.0, 22-24 November 2021, Saint-Étienne, France.

Full text available as:

Supporting Predictive Maintenance in Virtual Factory_Final Version.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1007/978-3-030-85969-5_13


In the context of collaborative manufacturing networks 4.0, Industry 4.0 drives the manufacturing-related processes, shifting conventional processes from one organization to collaborative processes across different organizations. In the manufacturing collaborative network, product design processes, manufacturing processes, maintenance processes should be integrated across different factories and enterprises. The collaborative manufacturing network 4.0 allows the amalgamation of manufacturing resources in multiple organizations to operate processes in a collaborative manner for reacting to the fast changes of markets or emergencies. In this paper, we propose a predictive maintenance service as a part of a virtual factory, a form of collaborative manufacturing network. Data-driven predictive maintenance service is built-in FIWARE, an industry 4.0 framework. To optimize predictive maintenance services based on different criteria within a virtual factor, such as geographical locations, similar types of machinery, or cost/time efficiency, etc., we provide our design and implementation to deal with providing better maintenance services and data exchanging across different collaborative partners with different requirements and modularizing of related functions.

Item Type:Conference or Workshop Item (Paper)
Additional Information:22nd IFIP WG 5.5 Working Conference on Virtual Enterprises
Uncontrolled Keywords:Virtual factory; Predictive maintenance; Maintenance schedule; Industry 4.0; Collaborative networks 4.0
Group:Faculty of Science & Technology
ID Code:36197
Deposited By: Symplectic RT2
Deposited On:06 Nov 2021 11:52
Last Modified:29 Apr 2022 09:25


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