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Cloud-enhanced predictive maintenance
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. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-8679-8049
2016 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015Article in journal (Refereed) Epub ahead of print
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, 2016.
Keyword [en]
Predictive maintenance, Condition-based maintenance, Context awareness, Cloud manufacturing
National Category
Reliability and Maintenance
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-12331DOI: 10.1007/s00170-016-8983-8OAI: oai:DiVA.org:his-12331DiVA: diva2:933621
Available from: 2016-06-07 Created: 2016-06-07 Last updated: 2016-10-18Bibliographically approved

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Schmidt, BernardWang, Lihui
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School of Engineering ScienceThe Virtual Systems Research Centre
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