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Statistical anomaly detection for train fleets
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Swedish Institute of Computer Science, Sweden.ORCID-id: 0000-0001-8577-6745
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2013 (Engelska)Ingår i: The AI Magazine, ISSN 0738-4602, Vol. 34, nr 1, s. 33-42Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

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

Ort, förlag, år, upplaga, sidor
2013. Vol. 34, nr 1, s. 33-42
Nyckelord [en]
Anomaly detection, Bayesian, Bayesian statistics, Maintenance cost, Normal behavior, Railway operators, Statistical anomaly detection, Train fleets, Condition monitoring, Maintenance
Nationell ämneskategori
Data- och informationsvetenskap
Identifikatorer
URN: urn:nbn:se:kth:diva-127174ISI: 000336891700004Scopus ID: 2-s2.0-84876175602OAI: oai:DiVA.org:kth-127174DiVA, id: diva2:643833
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QC 20130828

Tillgänglig från: 2013-08-28 Skapad: 2013-08-28 Senast uppdaterad: 2018-01-11Bibliografiskt granskad

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Holst, Anders

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