Supporting Predictive Maintenance in Virtual Factory
Authors: Sang, G.M., Xu, L. and de Vrieze, P.
Journal: IFIP Advances in Information and Communication Technology
Volume: 629 IFIPAICT
Pages: 151-160
eISSN: 1868-422X
ISBN: 9783030859688
ISSN: 1868-4238
DOI: 10.1007/978-3-030-85969-5_13
Abstract:In Industry 4.0 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.
https://eprints.bournemouth.ac.uk/36197/
Source: Scopus
Supporting Predictive Maintenance in Virtual Factory
Authors: Sang, G.M., Xu, L. and de Vrieze, P.
Journal: SMART AND SUSTAINABLE COLLABORATIVE NETWORKS 4.0 (PRO-VE 2021)
Volume: 629
Pages: 151-160
eISSN: 1868-422X
ISSN: 1868-4238
DOI: 10.1007/978-3-030-85969-5_13
https://eprints.bournemouth.ac.uk/36197/
Source: Web of Science (Lite)
Supporting Predictive Maintenance for Virtual Factory
Authors: Xu, L. and De Vrieze, P.
Conference: 22nd IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2021
Dates: 22-24 November 2021
Abstract: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.
https://eprints.bournemouth.ac.uk/36197/
Source: Manual
Supporting Predictive Maintenance in Virtual Factory
Authors: Sang, G.M., Xu, L. and de Vrieze, P.T.
Conference: PRO-VE 2021, Smart and Sustainable Collaborative Networks 4.0
Abstract: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.
https://eprints.bournemouth.ac.uk/36197/
Source: BURO EPrints