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Predictive Maintenance of Machine Tool Linear Axes: A Case from Manufacturing Industry
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.ORCID iD: 0000-0001-8679-8049
2018 (English)In: Procedia Manufacturing, Elsevier B.V. , 2018, p. 118-125Conference paper, Published paper (Refereed)
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 B.V. , 2018. p. 118-125
Keywords [en]
Condition Monitoring, Machine Tool, Predictive Maintenance
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:kth:diva-247425DOI: 10.1016/j.promfg.2018.10.022Scopus ID: 2-s2.0-85060444616OAI: oai:DiVA.org:kth-247425DiVA, id: diva2:1313107
Conference
28th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2018, 11 June 2018 through 14 June 2018
Note

QC20190502

Available from: 2019-05-02 Created: 2019-05-02 Last updated: 2019-05-02Bibliographically approved

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Publisher's full textScopushttps://www.faim2018.org/

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Wang, Lihui

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CiteExportLink to record
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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