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Article: A computational intelligence approach to railway track intervention planning

TitleA computational intelligence approach to railway track intervention planning
Authors
Issue Date2008
PublisherSpringer Verlag
Citation
Studies in Computational Intelligence, 2008, v. 86, p. 163-198 How to Cite?
AbstractRailway track intervention planning is the process of specifying the location and time of required maintenance and renewal activities. To facilitate the process, decision support tools have been developed and typically use an expert system built with rules specified by track maintenance engineers. However, due to the complex interrelated nature of component deterioration, it is problematic for an engineer to consider all combinations of possible deterioration mechanisms using a rule based approach. To address this issue, this chapter describes an approach to the intervention planning using a variety of computational intelligence techniques. The proposed system learns rules for maintenance planning from historical data and incorporates future data to update the rules as they become available thus the performance of the system improves over time. To determine the failure type, historical deterioration patterns of sections of track are first analyzed. A Rival Penalized Competitive Learning algorithm is then used to determine possible failure types. We have devised a generalized two stage evolutionary algorithm to produce curve functions for this purpose. The approach is illustrated using an example with real data which demonstrates that the proposed methodology is suitable and effective for the task in hand. © 2008 Springer-Verlag Berlin Heidelberg.
Persistent Identifierhttp://hdl.handle.net/10722/90687
ISSN
2015 SCImago Journal Rankings: 0.187
References

 

DC FieldValueLanguage
dc.contributor.authorBartram, Den_HK
dc.contributor.authorBurrow, Men_HK
dc.contributor.authorYao, Xen_HK
dc.date.accessioned2010-09-17T10:06:48Z-
dc.date.available2010-09-17T10:06:48Z-
dc.date.issued2008en_HK
dc.identifier.citationStudies in Computational Intelligence, 2008, v. 86, p. 163-198en_HK
dc.identifier.issn1860-949Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/90687-
dc.description.abstractRailway track intervention planning is the process of specifying the location and time of required maintenance and renewal activities. To facilitate the process, decision support tools have been developed and typically use an expert system built with rules specified by track maintenance engineers. However, due to the complex interrelated nature of component deterioration, it is problematic for an engineer to consider all combinations of possible deterioration mechanisms using a rule based approach. To address this issue, this chapter describes an approach to the intervention planning using a variety of computational intelligence techniques. The proposed system learns rules for maintenance planning from historical data and incorporates future data to update the rules as they become available thus the performance of the system improves over time. To determine the failure type, historical deterioration patterns of sections of track are first analyzed. A Rival Penalized Competitive Learning algorithm is then used to determine possible failure types. We have devised a generalized two stage evolutionary algorithm to produce curve functions for this purpose. The approach is illustrated using an example with real data which demonstrates that the proposed methodology is suitable and effective for the task in hand. © 2008 Springer-Verlag Berlin Heidelberg.en_HK
dc.languageengen_HK
dc.publisherSpringer Verlagen_HK
dc.relation.ispartofStudies in Computational Intelligenceen_HK
dc.titleA computational intelligence approach to railway track intervention planningen_HK
dc.typeArticleen_HK
dc.identifier.emailBurrow, MF:mfburr58@hku.hken_HK
dc.identifier.authorityBurrow, MF=rp1306en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-540-75771-9_8en_HK
dc.identifier.scopuseid_2-s2.0-39049133003en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-39049133003&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume86en_HK
dc.identifier.spage163en_HK
dc.identifier.epage198en_HK

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