TY - GEN
T1 - Unsupervised methods for anomalies detection through intelligent monitoring systems
AU - Carrascal, Alberto
AU - Díez, Alberto
AU - Azpeitia, Ander
PY - 2009
Y1 - 2009
N2 - The success of intelligent diagnosis systems normally depends on the knowledge about the failures present on monitored systems. This knowledge can be modelled in several ways, such as by means of rules or probabilistic models. These models are validated by checking the system output fit to the input in a supervised way. However, when there is no such knowledge or when it is hard to obtain a model of it, it is alternatively possible to use an unsupervised method to detect anomalies and failures. Different unsupervised methods (HCL, K-Means, SOM) have been used in present work to identify abnormal behaviours on the system being monitored. This approach has been tested into a real-world monitored system related to the railway domain, and the results show how it is possible to successfully identify new abnormal system behaviours beyond those previously modelled well-known problems.
AB - The success of intelligent diagnosis systems normally depends on the knowledge about the failures present on monitored systems. This knowledge can be modelled in several ways, such as by means of rules or probabilistic models. These models are validated by checking the system output fit to the input in a supervised way. However, when there is no such knowledge or when it is hard to obtain a model of it, it is alternatively possible to use an unsupervised method to detect anomalies and failures. Different unsupervised methods (HCL, K-Means, SOM) have been used in present work to identify abnormal behaviours on the system being monitored. This approach has been tested into a real-world monitored system related to the railway domain, and the results show how it is possible to successfully identify new abnormal system behaviours beyond those previously modelled well-known problems.
KW - Clustering
KW - Intelligent Monitoring Systems
KW - Unsupervised Anomaly Detection
KW - Unsupervised Classification
UR - http://www.scopus.com/inward/record.url?scp=70350646774&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-02319-4_17
DO - 10.1007/978-3-642-02319-4_17
M3 - Conference contribution
AN - SCOPUS:70350646774
SN - 3642023185
SN - 9783642023187
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 137
EP - 144
BT - Hybrid Artificial Intelligence Systems - 4th International Conference, HAIS 2009, Proceedings
T2 - 4th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2009
Y2 - 10 June 2009 through 12 June 2009
ER -