Fuzzy Anomaly Detection in Monitoring Sensor Data

Julien Rabatel 1, * Sandra Bringay 1 Pascal Poncelet 1
* Corresponding author
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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [9 references]  Display  Hide  Download

https://hal-lirmm.ccsd.cnrs.fr/lirmm-00503132
Contributor : Julien Rabatel <>
Submitted on : Monday, March 25, 2019 - 12:40:52 PM
Last modification on : Friday, October 25, 2019 - 1:07:54 PM
Long-term archiving on : Wednesday, June 26, 2019 - 2:39:53 PM

File

FIEEE2010.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Julien Rabatel, Sandra Bringay, Pascal Poncelet. Fuzzy Anomaly Detection in Monitoring Sensor Data. FUZZ-IEEE, Jul 2010, Barcelone, Spain. ⟨10.1109/FUZZY.2010.5584253⟩. ⟨lirmm-00503132⟩

Share

Metrics

Record views

148

Files downloads

58