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SVM based diagnosti...
SVM based diagnostics on railway turnouts
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- Eker, O.F. (författare)
- Meliksah University, Kayseri, Turkey
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- Camci, F. (författare)
- Meliksah University, Kayseri, Turkey; Integrated Vehicle Health Management Centre, Cranfield University, United Kingdom
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- Kumar, Uday (författare)
- Luleå tekniska universitet,Drift, underhåll och akustik
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(creator_code:org_t)
- 2012
- 2012
- Engelska.
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Ingår i: International Journal of Performability Engineering. - 0973-1318. ; 8:3, s. 289-298
- Relaterad länk:
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http://www.ijpe-onli...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- Railway turnout systems are one of the most critical pieces of equipment in railway infrastructure. Early identification of failures in turnout systems is important to obtain increased availability and safety, and reduced operating and support costs. This paper aims to develop a method to identify 'drive-rod out-of-adjustment' failure mode, one of the most frequently observed failure modes. Support Vector Machine (SVM) with Gaussian kernel is used for diagnosis. In addition, the results of feature selection with statistical t-test and feature reduction with principal component analysis (PCA) are compared in the paper
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Annan samhällsbyggnadsteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Other Civil Engineering (hsv//eng)
Nyckelord
- Operation and Maintenance Engineering
- Drift och underhållsteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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