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Big Data in Asset Management: Knowledge Discovery in Asset Data by the Means of Data Mining
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.ORCID-id: 0000-0002-4107-0991
Linnaeus University.
University of Skövde.
2016 (engelsk)Inngår i: Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015) / [ed] Kari T. Koskinen; Helena Kortelainen; Jussi Aaltonen; Teuvo Uusitalo; Kari Komonen; Joseph Mathew; Jouko Laitinen, Encyclopedia of Global Archaeology/Springer Verlag, 2016, s. 161-171Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Encyclopedia of Global Archaeology/Springer Verlag, 2016. s. 161-171
Serie
Lecture Notes in Mechanical Engineering, ISSN 2195-4356
HSV kategori
Forskningsprogram
Drift och underhållsteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-35605DOI: 10.1007/978-3-319-27064-7_16ISI: 000375993100016Lokal ID: a31c6aea-b510-450e-b6a9-5bce60d7275eISBN: 978-3-319-27062-3 (tryckt)ISBN: 978-3-319-27064-7 (digital)OAI: oai:DiVA.org:ltu-35605DiVA, id: diva2:1008858
Konferanse
World Congress on Engineering Asset Management : 28/09/2015 - 30/09/2015
Merknad
Validerad; 2016; Nivå 1; 20160601 (andbra)Tilgjengelig fra: 2016-09-30 Laget: 2016-09-30 Sist oppdatert: 2018-07-10bibliografisk kontrollert

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Totalt: 207 treff
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