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SO_MAD: SensOr Mining for Anomaly Detection in Railway Data

Julien Rabatel 1 Sandra Bringay 1 Pascal Poncelet 1
1 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
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.
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Julien Rabatel, Sandra Bringay, Pascal Poncelet. SO_MAD: SensOr Mining for Anomaly Detection in Railway Data. ICDM: Industrial Conference on Data Mining, Jul 2009, Leipzig, Germany. pp.191-205, ⟨10.1007/978-3-642-03067-3_16⟩. ⟨lirmm-00394298⟩



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