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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Rabatel, J., Bringay, S., Poncelet, P. (2009). SO_MAD: SensOr Mining for Anomaly Detection in Railway Data. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2009. Lecture Notes in Computer Science(), vol 5633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03067-3_16
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DOI: https://doi.org/10.1007/978-3-642-03067-3_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03066-6
Online ISBN: 978-3-642-03067-3
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