Applying Predictive Maintenance in Flexible Manufacturing
Authors: Sang, G.M., Xu, L., de Vrieze, P. and Bai, Y.
Journal: IFIP Advances in Information and Communication Technology
Volume: 598
Pages: 203-212
eISSN: 1868-422X
ISBN: 9783030624118
ISSN: 1868-4238
DOI: 10.1007/978-3-030-62412-5_17
Abstract: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.
https://eprints.bournemouth.ac.uk/34888/
Source: Scopus
Applying Predictive Maintenance in Flexible Manufacturing
Authors: Sang, G.M., Xu, L., de Vrieze, P. and Bai, Y.
Journal: BOOSTING COLLABORATIVE NETWORKS 4.0
Volume: 598
Pages: 203-212
eISSN: 1868-422X
ISSN: 1868-4238
DOI: 10.1007/978-3-030-62412-5_17
https://eprints.bournemouth.ac.uk/34888/
Source: Web of Science (Lite)
Applying Predictive Maintenance in Flexible Manufacturing
Authors: Xu, L., De Vrieze, P. and Bai, Y.
Conference: PRO-VE 2020. IFIP Advances in Information and Communication Technology
Dates: 23-25 November 2020
Abstract: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.
https://eprints.bournemouth.ac.uk/34888/
Source: Manual
Applying Predictive Maintenance in Flexible Manufacturing
Authors: Sang, G.M., Xu, L., de Vrieze, P.T. and Bai, Y.
Editors: Camarinha-Matos, L.M., Afsarmanesh, H. and Ortiz, A.
Conference: Boosting Collaborative Networks 4.0: 21st IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2020
Pages: 203-212
Publisher: IFIP Advances in Information and Communication Technology, Springer
ISBN: 978-3-030-62412-5
ISSN: 1868-4238
Abstract: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.
https://eprints.bournemouth.ac.uk/34888/
https://link.springer.com/chapter/10.1007/978-3-030-62412-5_17
Source: BURO EPrints