Skip to main content

Applying Predictive Maintenance in Flexible Manufacturing.

Sang, G.M., Xu, L., de Vrieze, P. T. and Bai, Y., 2020. Applying Predictive Maintenance in Flexible Manufacturing. In: Boosting Collaborative Networks 4.0: 21st IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2020, 23 - 25 November 2020, Valencia, Spain (held online), 203-212.

Full text available as:

camera ready - 18 August 2020.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


Official URL:


In Industry 4.0 context, manufacturing related processes e.g. design processes, maintenance processes are collaboratively processed across different factories and enterprises. The state i.e. operation, failures of production equipment tools could easily impact on the collaboration and related processes. This complex collaboration requires a flexible and extensible system architecture and platform, to support dynamic collaborations with advanced capabilities such as big data analytics for maintenance. As such, this paper looks at how to support data-driven and flexible predictive maintenance in collaboration using FIWARE? Especially, applying big data analytics and data-driven approach for effective maintenance schedule plan, employing FIWARE Framework, which leads to support collaboration among different organizations modularizing of different related functions and security requirements.

Item Type:Conference or Workshop Item (Paper)
Additional Information:This research is partially funded by the State Key Research and Development Program of China (2017YFE0118700) and it is part of the FIRST project which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 734599.
Uncontrolled Keywords:Collaboration; Predictive Maintenance; Maintenance Schedule Plan; Industry 4.0.
Group:Faculty of Science & Technology
ID Code:34888
Deposited By: Symplectic RT2
Deposited On:26 Nov 2020 11:14
Last Modified:14 Mar 2022 14:25


Downloads per month over past year

More statistics for this item...
Repository Staff Only -