Fuzzy Anomaly Detection in Monitoring Sensor Data

Julien Rabatel 1, * Sandra Bringay 1 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 challenges raised by maintenance. In particular, the anomaly detection problem is probably one of the most investigated. In this paper we address anomaly detection in new train data by comparing them to a source of normal train behavior knowledge, expressed as sequential patterns. To this end, fuzzy logic allows our approach to be both finer and easier to interpret for experts. In order to show the quality of our approach, experiments have been conducted on real and simulated anomalies.
Type de document :
Communication dans un congrès
FUZZ: Fuzzy Systems, Jul 2010, Barcelone, Spain. 19th IEEE International Conference on Fuzzy Systems, 2010, 〈http://www.wcci2010.org/〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00503132
Contributeur : Julien Rabatel <>
Soumis le : vendredi 16 juillet 2010 - 16:17:43
Dernière modification le : jeudi 24 mai 2018 - 15:59:22

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  • HAL Id : lirmm-00503132, version 1

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Julien Rabatel, Sandra Bringay, Pascal Poncelet. Fuzzy Anomaly Detection in Monitoring Sensor Data. FUZZ: Fuzzy Systems, Jul 2010, Barcelone, Spain. 19th IEEE International Conference on Fuzzy Systems, 2010, 〈http://www.wcci2010.org/〉. 〈lirmm-00503132〉

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