Anomaly Detection in Monitoring Sensor Data for Preventive Maintenance - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier
Article Dans Une Revue Expert Systems with Applications Année : 2011

Anomaly Detection in Monitoring Sensor Data for Preventive Maintenance

Résumé

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.
Fichier principal
Vignette du fichier
Rabatel_ESWA2011.pdf (746.4 Ko) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

lirmm-00670917 , version 1 (16-02-2012)

Identifiants

Citer

Julien Rabatel, Sandra Bringay, Pascal Poncelet. Anomaly Detection in Monitoring Sensor Data for Preventive Maintenance. Expert Systems with Applications, 2011, 38 (6), pp.7003-7015. ⟨10.1016/j.eswa.2010.12.014⟩. ⟨lirmm-00670917⟩
306 Consultations
3117 Téléchargements

Altmetric

Partager

More