DocumentCode :
2306146
Title :
Fuzzy anomaly detection in monitoring sensor data
Author :
Rabatel, Julien ; Bringay, Sandra ; Poncelet, Pascal
Author_Institution :
LIRMM, Univ. Montpellier 2, Montpellier, France
fYear :
2010
fDate :
18-23 July 2010
Firstpage :
1
Lastpage :
8
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.
Keywords :
fuzzy logic; preventive maintenance; production engineering computing; security of data; fuzzy anomaly detection; fuzzy logic; industrial companies; sensor data monitoring; Data mining; Itemsets; Maintenance engineering; Monitoring; Rail transportation; Temperature measurement; Temperature sensors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems (FUZZ), 2010 IEEE International Conference on
Conference_Location :
Barcelona
ISSN :
1098-7584
Print_ISBN :
978-1-4244-6919-2
Type :
conf
DOI :
10.1109/FUZZY.2010.5584253
Filename :
5584253
Link To Document :
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