Statistical Anomaly Detection for Train Fleets

Bohlin, MarkusMälardalens högskola,RISE,SICS,Akademin för innovation, design och teknik,IS (author)

Accepted for publication.
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.

Holst, AndersRISE,Decisions, Networks and Analytics lab,Swedish Institute of Computer Science(Swepub:ri)AndersHol@ri.se (author)
Ekman, JanRISE,Decisions, Networks and Analytics lab,Swedish Institute of Computer Science(Swepub:ri)jan.ekman@ri.se (author)
Sellin, OlaBombardier Transportation, Sweden (author)
Lindström, BjörnAddiva Consulting AB, Sweden (author)
Larsen, StefanAddiva Eduro AB, Sweden (author)
RISESICS (creator_code:org_t)

In:Proceedings of the National Conference on Artificial Intelligence, vol. 3, 2012Toronto, Canada, s. 2217-22239781577355687

By the author/editor: Bohlin, Markus; Holst, Anders; Ekman, Jan; Sellin, Ola; Lindström, Björn; Larsen, Stefan

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