Evolutionary generation of fuzzy knowledge bases for diagnosing monitored railway systems

Carrascal, Alberto and Díez Oliván, Alberto and Font Fernández, José María and Manrique Gamo, Daniel (2009). Evolutionary generation of fuzzy knowledge bases for diagnosing monitored railway systems. In: "22nd International Congress on Condition monitoring and diagnostic engineering management (COMADEM 2009)", 09-11 Jun 2009, San Sebastián, España. ISBN 978-84-932064-6-8. pp. 191-198.

Description

Title: Evolutionary generation of fuzzy knowledge bases for diagnosing monitored railway systems
Author/s:
  • Carrascal, Alberto
  • Díez Oliván, Alberto
  • Font Fernández, José María
  • Manrique Gamo, Daniel
Item Type: Presentation at Congress or Conference (Article)
Event Title: 22nd International Congress on Condition monitoring and diagnostic engineering management (COMADEM 2009)
Event Dates: 09-11 Jun 2009
Event Location: San Sebastián, España
Title of Book: Condition monitoring and diagnostic engineering management; COMADEM
Date: 2009
ISBN: 978-84-932064-6-8
Volume: 22
Subjects:
Freetext Keywords: Monitoring and diagnosis systems; Grammar guided ge netic programming; Fuzzy logic; Rule-based system; Evolutionary computation; Knowledge discovery.
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Classical approaches when building diagnosis and monitoring systems are rule-based systems, which allow the representation of existing knowledge by using rules. There are several techniques that facilitate this task, such as fuzzy logic, which allows knowledge to be modeled in an intuitive way. Nevertheless, sometimes it is not easy to define the fuzzy rule set that represents the knowledge from a certain domain. To overcome this drawback, an evolutionary system based on a grammar guided genetic programming technique for the automatic generation of fuzzy knowledge bases has been employed in diagnosing monitored railway networks. This system employs a grammar-based initialization method and both, grammar-based crossover and mutation operators, to achieve well balanced exploitation and exploration capabilities of the search space, assuring high convergence speed and low chance of getting trapped in local optima. Tests have been carried out in a real-world train monitoring domain, in which a sensor network is periodically monitoring critical train components. Results achieved show that this evolutionary system accomplishes an automatic knowledge discovery process, which is able to build a fuzzy rule base that represents the expert knowledge extracted from the domain of the detection of abnormal train conditions.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainEP2005-00232-C03-03UnspecifiedUnspecifiedUnspecified

More information

Item ID: 46862
DC Identifier: http://oa.upm.es/46862/
OAI Identifier: oai:oa.upm.es:46862
Official URL: http://www.comadem.com/
Deposited by: Memoria Investigacion
Deposited on: 04 Apr 2018 10:18
Last Modified: 04 Apr 2018 10:18
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