Unsupervised learning for opportunistic maintenance optimization in flexible manufacturing systems

Wocker, M; Betz, NK; Feuersänger, C; Lindworsky, A; Deuse, J

Unsupervised learning for opportunistic maintenance optimization in flexible manufacturing systems

Wocker, M Betz, NK Feuersänger, C Lindworsky, A Deuse, J

Permalink

Publisher:: Elsevier BV
Publication Type:: Conference Proceeding
Citation:: Procedia CIRP, 2020, 93, pp. 1025-1030
Issue Date:: 2020-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (1.02 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Wocker, M
dc.contributor.author	Betz, NK
dc.contributor.author	Feuersänger, C
dc.contributor.author	Lindworsky, A
dc.contributor.author	Deuse, J https://orcid.org/0000-0003-4066-4357
dc.date.accessioned	2021-05-31T21:55:07Z
dc.date.available	2021-05-31T21:55:07Z
dc.date.issued	2020-01-01
dc.identifier.citation	Procedia CIRP, 2020, 93, pp. 1025-1030
dc.identifier.issn	2212-8271
dc.identifier.uri	http://hdl.handle.net/10453/149313
dc.description.abstract	Large scale manufacturing systems with a high degree in automation and the ability to produce several product variants in parallel meet current requirements of a highly flexible and at the same time productive manufacturing process. In practice, however, the non-transparency as well as the complexity of these systems overwhelm the maintenance department in the effective planning and implementation of maintenance tasks. As a result, major maintenance tasks are postponed to non-production times which causes increased maintenance cost as well as a decrease in system availability. This research explores a method that uses unsupervised learning algorithms to analyze type mixes and related process performances inside the system. The information is used to determine the optimal master production schedule prior to maintenance activities which leads to more frequent and extended time windows for maintenance activities during production time and thus to an increase in system availability.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Procedia CIRP
dc.relation.isbasedon	10.1016/j.procir.2020.04.025
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Unsupervised learning for opportunistic maintenance optimization in flexible manufacturing systems
dc.type	Conference Proceeding
utslib.citation.volume	93
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 Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2021-05-31T21:54:55Z
pubs.publication-status	Published
pubs.volume	93

Abstract:

Large scale manufacturing systems with a high degree in automation and the ability to produce several product variants in parallel meet current requirements of a highly flexible and at the same time productive manufacturing process. In practice, however, the non-transparency as well as the complexity of these systems overwhelm the maintenance department in the effective planning and implementation of maintenance tasks. As a result, major maintenance tasks are postponed to non-production times which causes increased maintenance cost as well as a decrease in system availability. This research explores a method that uses unsupervised learning algorithms to analyze type mixes and related process performances inside the system. The information is used to determine the optimal master production schedule prior to maintenance activities which leads to more frequent and extended time windows for maintenance activities during production time and thus to an increase in system availability.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/149313