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Statistical Anomaly Detection for Train Fleets
RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.ORCID iD: 0000-0003-1597-6738
RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.ORCID iD: 0000-0001-8577-6745
RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.ORCID iD: 0000-0002-7181-8411
Bombardier Transportation, Sweden.
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2012 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We have developed a method for statistical anomaly detection which has been deployed in a tool for condition monitoring of train fleets. The tool is currently used by several railway operators over the world to inspect and visualize the occurrence of "event messages" generated on the trains. The anomaly detection component helps the operators to quickly find significant deviations from normal behavior and to detect early indications for possible problems. The savings in maintenance costs comes mainly from avoiding costly breakdowns, and have been estimated to several million Euros per year for the tool. In the long run, it is expected that maintenance costs can be reduced with between 5 and 10 % by using the tool.

Place, publisher, year, edition, pages
Toronto, Canada, 2012, 9. Vol. 3, p. 2217-2223
Keywords [en]
Anomaly detection, Maintenance cost, Normal behavior, Railway operators, Statistical anomaly detection, Train fleets
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-24031Scopus ID: 2-s2.0-84868290884ISBN: 9781577355687 (print)OAI: oai:DiVA.org:ri-24031DiVA, id: diva2:1043110
Conference
Proceedings of the 21st Innovative Applications of Artificial Intelligence Conference
Projects
DUST
Note

Accepted for publication.

Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2023-05-09Bibliographically approved

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Bohlin, MarkusHolst, AndersEkman, Jan

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CiteExportLink to record
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Citation style
  • apa
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Language
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  • en-US
  • fi-FI
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  • nn-NB
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Output format
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