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Predictive Maintenance in Industry 4.0.

Sang, G.M., Xu, L., de Vrieze, P. T., Bai, Y. and Pan, F., 2020. Predictive Maintenance in Industry 4.0. In: ICIST 2020: 10th International Conference on Information Systems and Technologies, 4-5 June 2020, Lecce, Italy. (In Press)

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DOI: 10.1145/1234567890

Abstract

In the context of Industry 4.0, the manufacturing-related processes have shifted from conventional processes within one organization to collaborative processes cross different organizations, for example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. The development and application of the Internet of things, i.e. smart devices and sensors increases the availability and collection of diverse data. With new technologies, such as advanced data analytics and cloud computing provide new opportunities for flexible collaborations as well as effective optimizing manufacturing-related processes, e.g. predictive maintenance. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machinery using various analyses. RAMI4.0 is a framework for thinking about the various efforts that constitute Industry 4.0. It spans the entire product life cycle & value stream axis, hierarchical structure axis and functional classification axis. The Industrial Data Space (now International Data Space) is a virtual data space using standards and common governance models to facilitate the secure exchange and easy linkage of data in business ecosystems. It thereby provides a basis for creating and using smart services and innovative business processes, while at the same time ensuring digital sovereignty of data owners. This paper looks at how to support predictive maintenance in the context of Industry 4.0. Especially, applying RAMI4.0 architecture supports the predictive maintenance using the FIWARE framework, which leads to deal with data exchanging among different organizations with different security requirements as well as modularizing of related functions.

Item Type:Conference or Workshop Item (Paper)
ISSN:1552-5996
Group:Faculty of Science & Technology
ID Code:33017
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:11 Nov 2019 09:43
Last Modified:15 Nov 2019 16:45

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