Anomaly Detection in Monitoring Sensor Data for Preventive Maintenance

Julien Rabatel 1, * Sandra Bringay 1, 2 Pascal Poncelet 1
* Auteur correspondant
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 the exact localization of a detected anomaly.
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Article dans une revue
Expert Systems with Applications, Elsevier, 2011, 38 (6), pp.7003-7015. 〈http://www.sciencedirect.com/science/article/pii/S0957417410013771〉. 〈10.1016/j.eswa.2010.12.014〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00670917
Contributeur : Julien Rabatel <>
Soumis le : jeudi 16 février 2012 - 13:27:43
Dernière modification le : jeudi 24 mai 2018 - 15:59:23
Document(s) archivé(s) le : jeudi 17 mai 2012 - 02:26:28

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Julien Rabatel, Sandra Bringay, Pascal Poncelet. Anomaly Detection in Monitoring Sensor Data for Preventive Maintenance. Expert Systems with Applications, Elsevier, 2011, 38 (6), pp.7003-7015. 〈http://www.sciencedirect.com/science/article/pii/S0957417410013771〉. 〈10.1016/j.eswa.2010.12.014〉. 〈lirmm-00670917〉

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