Unsupervised Methods for Anomalies Detection through Intelligent Monitoring Systems

Carrascal, Alberto and Diez Oliván, Alberto and Azpeitia, Ander (2009). Unsupervised Methods for Anomalies Detection through Intelligent Monitoring Systems. In: "Hybrid Artificial Intelligence Systems 4th International Conference, HAIS 2009", 10-12 June 2009, Salamanca (SPAIN). ISBN 978-3-642-02319-4. pp. 137-144. https://doi.org/10.1007/978-3-642-02319-4_17.

Description

Title: Unsupervised Methods for Anomalies Detection through Intelligent Monitoring Systems
Author/s:
  • Carrascal, Alberto
  • Diez Oliván, Alberto
  • Azpeitia, Ander
Item Type: Presentation at Congress or Conference (Article)
Event Title: Hybrid Artificial Intelligence Systems 4th International Conference, HAIS 2009
Event Dates: 10-12 June 2009
Event Location: Salamanca (SPAIN)
Title of Book: Hybrid Artificial Intelligence Systems 4th International Conference, HAIS 2009. Proceedings
Date: 2009
ISBN: 978-3-642-02319-4
Subjects:
Freetext Keywords: Unsupervised Anomaly Detection, Unsupervised Classification, Intelligent Monitoring Systems, Clustering.
Faculty: E.T.S.I. Industriales (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 46872
DC Identifier: http://oa.upm.es/46872/
OAI Identifier: oai:oa.upm.es:46872
DOI: 10.1007/978-3-642-02319-4_17
Official URL: https://link.springer.com/chapter/10.1007%2F978-3-642-02319-4_17
Deposited by: Memoria Investigacion
Deposited on: 22 Jun 2017 16:14
Last Modified: 22 Jun 2017 16:14
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