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Conference Papers Year : 2009

SO_MAD: SensOr Mining for Anomaly Detection in Railway Data

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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Dates and versions

lirmm-00394298 , version 1 (02-04-2019)

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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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