SO_MAD: SensOr Mining for Anomaly Detection in Railway Data

Authors:
Julien Rabatel;Sandra Bringay;Pascal Poncelet
Affiliations:
LIRMM, Université Montpellier 2, CNRS, Montpellier Cedex 5, France 34392 and Fatronik France Tecnalia, Cap Omega, Rond-point Benjamin Franklin - CS 39521, Montpellier, France 34960;LIRMM, Université Montpellier 2, CNRS, Montpellier Cedex 5, France 34392 and Dpt MIAp, Université Montpellier 3, Montpellier Cedex 5, France 34199;LIRMM, Université Montpellier 2, CNRS, Montpellier Cedex 5, France 34392
Venue:
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Year:
2009

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Abstract

Today, many industrial companies must face problems raised by maintenance. In particular, the anomaly detection problem is probably one of the most challenging. In this paper we focus on the railway maintenance task and propose to automatically detect anomalies in order to predict in advance potential failures. We first address the problem of characterizing normal behavior. In order to extract interesting patterns, we have developed a method to take into account the contextual criteria associated to railway data (itinerary, weather conditions, etc.). We then measure the compliance of new data, according to extracted knowledge, and provide information about the seriousness and possible causes of a detected anomaly.