A Manufacturing Big Data Solution for Active Preventive Maintenance

Wan, J; Tang, S; Li, D; Wang, S; Liu, C; Abbas, H; Vasilakos, AV

A Manufacturing Big Data Solution for Active Preventive Maintenance

Wan, J Tang, S Li, D Wang, S Liu, C Abbas, H Vasilakos, AV

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Industrial Informatics, 2017, 13, (4), pp. 2039-2047
Issue Date:: 2017-08-01

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Field	Value	Language
dc.contributor.author	Wan, J
dc.contributor.author	Tang, S
dc.contributor.author	Li, D
dc.contributor.author	Wang, S
dc.contributor.author	Liu, C
dc.contributor.author	Abbas, H
dc.contributor.author	Vasilakos, AV
dc.date.accessioned	2022-08-23T02:52:22Z
dc.date.available	2022-08-23T02:52:22Z
dc.date.issued	2017-08-01
dc.identifier.citation	IEEE Transactions on Industrial Informatics, 2017, 13, (4), pp. 2039-2047
dc.identifier.issn	1551-3203
dc.identifier.issn	1941-0050
dc.identifier.uri	http://hdl.handle.net/10453/160722
dc.description.abstract	Industry 4.0 has become more popular due to recent developments in cyber-physical systems, big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a revolutionary change, and evolving applications, such as product lifecycle management, are becoming a reality. In this paper, we propose and implement a manufacturing big data solution for active preventive maintenance in manufacturing environments. First, we provide the system architecture that is used for active preventive maintenance. Then, we analyze the method used for collection of manufacturing big data according to the data characteristics. Subsequently, we perform data processing in the cloud, including the cloud layer architecture, the real-time active maintenance mechanism, and the offline prediction and analysis method. Finally, we analyze a prototype platform and implement experiments to compare the traditionally used method with the proposed active preventive maintenance method. The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Industrial Informatics
dc.relation.isbasedon	10.1109/TII.2017.2670505
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Electrical & Electronic Engineering
dc.title	A Manufacturing Big Data Solution for Active Preventive Maintenance
dc.type	Journal Article
utslib.citation.volume	13
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-23T02:52:21Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	4

Abstract:

Industry 4.0 has become more popular due to recent developments in cyber-physical systems, big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a revolutionary change, and evolving applications, such as product lifecycle management, are becoming a reality. In this paper, we propose and implement a manufacturing big data solution for active preventive maintenance in manufacturing environments. First, we provide the system architecture that is used for active preventive maintenance. Then, we analyze the method used for collection of manufacturing big data according to the data characteristics. Subsequently, we perform data processing in the cloud, including the cloud layer architecture, the real-time active maintenance mechanism, and the offline prediction and analysis method. Finally, we analyze a prototype platform and implement experiments to compare the traditionally used method with the proposed active preventive maintenance method. The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.

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http://hdl.handle.net/10453/160722