his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Predictive Maintenance of Machine Tool Linear Axes: A Case from Manufacturing Industry
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0002-8906-630X
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-8679-8049
2018 (English)In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 17, p. 118-125Article in journal (Refereed) Published
Abstract [en]

In sustainable manufacturing, the proper maintenance is crucial to minimise the negative environmental impact. In the context of Cloud Manufacturing, Internet of Things and Big Data, amount of available information is not an issue, the problem is to obtain the relevant information and process them in a useful way. In this paper a maintenance decision support system is presented that utilises information from multiple sources and of a different kind. The key elements of the proposed approach are processing and machine learning method evaluation and selection, as well as estimation of long-term key performance indicators (KPIs) such as a ratio of unplanned breakdowns or a cost of maintenance approach. Presented framework is applied to machine tool linear axes. Statistical models of failures and Condition Based Maintenance (CBM) are built based on data from a population of 29 similar machines from the period of over 4 years and with use of proposed processing approach. Those models are used in simulation to estimate the long-term effect on selected KPIs for different strategies. Simple CBM approach allows, in the considered case, a cost reduction of 40% with the number of breakdowns reduced 6 times in respect to an optimal time-based approach.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 17, p. 118-125
Keywords [en]
predictive maintenance, condition monitoring, machine tool
National Category
Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-16410DOI: 10.1016/j.promfg.2018.10.022ISI: 000471035200015Scopus ID: 2-s2.0-85060444616OAI: oai:DiVA.org:his-16410DiVA, id: diva2:1264223
Conference
28th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2018) June 11-14, 2018, Columbus, OH, USA
Available from: 2018-11-19 Created: 2018-11-19 Last updated: 2019-06-27Bibliographically approved

Open Access in DiVA

fulltext(1423 kB)17 downloads
File information
File name FULLTEXT01.pdfFile size 1423 kBChecksum SHA-512
310a7891b4fbdeff99ea1a3d897ab6e70e4ee9ccb36f36070e4542830fce4a9a1ccc5a5023f249a466486727f137db31dcb626f8a615e5cd0987e6186b3c128c
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records BETA

Schmidt, BernardWang, Lihui

Search in DiVA

By author/editor
Schmidt, BernardWang, Lihui
By organisation
School of Engineering ScienceThe Virtual Systems Research Centre
In the same journal
Procedia Manufacturing
Reliability and Maintenance

Search outside of DiVA

GoogleGoogle Scholar
Total: 17 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 133 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf