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Towards Predictive Maintenance for Flexible Manufacturing Using FIWARE.

Sang, G.M., Xu, L., de Vrieze, P. T. and Bai, Y., 2020. Towards Predictive Maintenance for Flexible Manufacturing Using FIWARE. In: CAiSE 2020, 8-12 June 2020, Grenoble, France, 17 - 28.

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paper_278.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1007/978-3-030-49165-9_2


Industry 4.0 has shifted the manufacturing related processes from conventional processes within one organization to collaborative processes across different organizations. For example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. This complex and competitive collaboration requires the underlying system architecture and platform to be flexible and extensible to support the demands of dynamic collaborations as well as advanced functionalities such as big data analytics. Both operation and condition of the production equipment are critical to the whole manufacturing process. Failures of any machine tools can easily have impact on the subsequent value-added processes of the collaboration. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machineries using various analyses. In this context, this paper explores how the FIWARE framework supports predictive maintenance. Specifically, it looks at applying a data driven approach to the Long Short-Term Memory Network (LSTM) model for machine condition and remaining useful life to support predictive maintenance using FIWARE framework in a modular fashion.

Item Type:Conference or Workshop Item (Paper)
Group:Faculty of Science & Technology
ID Code:34118
Deposited By: Symplectic RT2
Deposited On:09 Jun 2020 11:12
Last Modified:14 Mar 2022 14:22


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