Unsupervised Methods for Anomalies Detection through Intelligent Monitoring Systems

Carrascal, Alberto; Diez Oliván, Alberto y Azpeitia, Ander (2009). Unsupervised Methods for Anomalies Detection through Intelligent Monitoring Systems. En: "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.

Descripción

Título: Unsupervised Methods for Anomalies Detection through Intelligent Monitoring Systems
Autor/es:
  • Carrascal, Alberto
  • Diez Oliván, Alberto
  • Azpeitia, Ander
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: Hybrid Artificial Intelligence Systems 4th International Conference, HAIS 2009
Fechas del Evento: 10-12 June 2009
Lugar del Evento: Salamanca (SPAIN)
Título del Libro: Hybrid Artificial Intelligence Systems 4th International Conference, HAIS 2009. Proceedings
Fecha: 2009
ISBN: 978-3-642-02319-4
Materias:
Palabras Clave Informales: Unsupervised Anomaly Detection, Unsupervised Classification, Intelligent Monitoring Systems, Clustering.
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 46872
Identificador DC: http://oa.upm.es/46872/
Identificador OAI: oai:oa.upm.es:46872
Identificador DOI: 10.1007/978-3-642-02319-4_17
URL Oficial: https://link.springer.com/chapter/10.1007%2F978-3-642-02319-4_17
Depositado por: Memoria Investigacion
Depositado el: 22 Jun 2017 16:14
Ultima Modificación: 22 Jun 2017 16:14
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