Anomaly detection in monitoring sensor data for preventive maintenance

Authors:
Julien Rabatel;Sandra Bringay;Pascal Poncelet
Affiliations:
LIRMM, Université Montpellier 2, CNRS, 161 rue Ada, 34392 Montpellier Cedex 5, France and Fatronik France Tecnalia Cap Omega, Rond-point Benjamin Franklin - CS 39521, 34960 Montpellier, Franc ...;LIRMM, Université Montpellier 2, CNRS, 161 rue Ada, 34392 Montpellier Cedex 5, France and Dpt MIAp, Université Montpellier 3, Route de Mende, 34199 Montpellier Cedex 5, France;LIRMM, Université Montpellier 2, CNRS, 161 rue Ada, 34392 Montpellier Cedex 5, France
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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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 the exact localization of a detected anomaly.