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

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