Enabling of Predictive Maintenance in the Brownfield through Low-Cost Sensors, an IIoT-Architecture and Machine Learning

Strauß, P; Schmitz, M; Wöstmann, R; Deuse, J

Enabling of Predictive Maintenance in the Brownfield through Low-Cost Sensors, an IIoT-Architecture and Machine Learning

Strauß, P Schmitz, M Wöstmann, R Deuse, J

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 2019, pp. 1474-1483
Issue Date:: 2019-01-22

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Field	Value	Language
dc.contributor.author	Strauß, P
dc.contributor.author	Schmitz, M
dc.contributor.author	Wöstmann, R
dc.contributor.author	Deuse, J
dc.date	2018-12-10
dc.date.accessioned	2020-06-15T23:09:23Z
dc.date.available	2020-06-15T23:09:23Z
dc.date.issued	2019-01-22
dc.identifier.citation	Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 2019, pp. 1474-1483
dc.identifier.isbn	9781538650356
dc.identifier.uri	http://hdl.handle.net/10453/141436
dc.description.abstract	© 2018 IEEE. Predictive maintenance is one of the major drivers of Industry 4.0 as it can significantly reduce costs by improving overall equipment effectiveness and extending the remaining useful life of production machines. Most of the potential lies in the brownfield with old equipment where no sensors or connectivity are available. This paper shows how these production machines can be enabled for predictive maintenance by retrofitting with low-cost sensors, an Industrial-Internet-of-Things-architecture and machine learning. An industrial implementation on a heavy lift Electric Monorail System at the BMW Group will be shown.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
dc.relation.ispartof	IEEE International Conference on Big Data
dc.relation.isbasedon	10.1109/BigData.2018.8622076
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Enabling of Predictive Maintenance in the Brownfield through Low-Cost Sensors, an IIoT-Architecture and Machine Learning
dc.type	Conference Proceeding
utslib.location.activity	Seattle, WA, USA
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 Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-15T23:09:20Z
pubs.finish-date	2018-12-13
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2018-12-10
dc.location	Piscataway, USA

Abstract:

© 2018 IEEE. Predictive maintenance is one of the major drivers of Industry 4.0 as it can significantly reduce costs by improving overall equipment effectiveness and extending the remaining useful life of production machines. Most of the potential lies in the brownfield with old equipment where no sensors or connectivity are available. This paper shows how these production machines can be enabled for predictive maintenance by retrofitting with low-cost sensors, an Industrial-Internet-of-Things-architecture and machine learning. An industrial implementation on a heavy lift Electric Monorail System at the BMW Group will be shown.

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http://hdl.handle.net/10453/141436