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Cloud-enhanced predictive maintenance
KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.ORCID iD: 0000-0001-8679-8049
2018 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 99, no 1-4, p. 5-13Article in journal (Refereed) Published
Abstract [en]

Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and makes decisions upon this prediction. The main aim of the present research is to achieve an improvement in predictive condition-based maintenance decision making through a cloud-based approach with usage of wide information content. For the improvement, it is crucial to identify and track not only condition related data but also context data. Context data allows better utilisation of condition monitoring data as well as analysis based on a machine population. The objective of this paper is to outline the first steps of a framework and methodology to handle and process maintenance, production, and factory related data from the first lifecycle phase to the operation and maintenance phase. Initial case study aims to validate the work in the context of real industrial applications.

Place, publisher, year, edition, pages
Springer London , 2018. Vol. 99, no 1-4, p. 5-13
Keywords [en]
Cloud manufacturing, Condition-based maintenance, Context awareness, Predictive maintenance, Decision making, Life cycle, Maintenance, Manufacture, Population statistics, Condition based maintenance, Condition-monitoring data, Context- awareness, Information contents, Manufacturing equipment, Operation and maintenance, Condition monitoring
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:kth:diva-247410DOI: 10.1007/s00170-016-8983-8ISI: 000445800600002Scopus ID: 2-s2.0-85061379865OAI: oai:DiVA.org:kth-247410DiVA, id: diva2:1313160
Note

QC20190205

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

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Schmidt, BjörnWang, Lihui

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