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Big data in asset management: Knowledge Discovery in asset data by the means of data mining
Luleå University of Technology.
Linnaeus University, Faculty of Technology, Department of Mechanical Engineering.
University of Skövde.
2016 (English)In: Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015) / [ed] Koskinen, KT; Kortelainen, H; Aaltonen, J; Uusitalo, T; Komonen, K; Mathew, J; Laitinen, J, Springer, 2016, 161-171 p.Conference paper, Published paper (Refereed)
Abstract [en]

Assets are complex mixes of complex systems, built from components which, over time, may fail. The ability to quickly and efficiently determine the cause of failures and propose optimum maintenance decisions, while minimizing the need for human intervention is necessary. Thus, for complex assets, much information needs to be captured and mined to assess the overall condition of the whole system. Therefore the integration of asset information is required to get an accurate health assessment of the whole system, and determine the probability of a shutdown or slowdown. Moreover, the data collected are not only huge but often dispersed across independent systems that are difficult to access, fuse and mine due to disparate nature and granularity. If the data from these independent systems are combined into a common correlated data source, this new set of information could add value to the individual data sources by the means of data mining. This paper proposes a knowledge discovery process based on CRISP-DM for failure diagnosis using big data sets. The process is exemplified by applying it on railway infrastructure assets. The proposed framework implies a progress beyond the state of the art in the development of Big Data technologies in the fields of Knowledge Discovery algorithms from heterogeneous data sources, scalable data structures, real-time communications and visualizations techniques.

Place, publisher, year, edition, pages
Springer, 2016. 161-171 p.
Series
Lecture Notes Mechanical Engineering, ISSN 2195-4356
National Category
Reliability and Maintenance
Research subject
Technology (byts ev till Engineering), Terotechnology
Identifiers
URN: urn:nbn:se:lnu:diva-50988DOI: 10.1007/978-3-319-27064-7_16ISI: 000375993100016ISBN: 978-3-319-27062-3 (print)OAI: oai:DiVA.org:lnu-50988DiVA: diva2:912752
Conference
10th World Congress on Engineering Asset Management (WCEAM), Tampere, FINLAND, SEP 28-30, 2015
Available from: 2016-03-17 Created: 2016-03-17 Last updated: 2016-06-10Bibliographically approved

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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
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  • en-US
  • fi-FI
  • nn-NO
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  • Other locale
More languages
Output format
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