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SVM Based Diagnostics on Railway Turnouts

Volume 8, Number 3, May 2012 - Paper 6 - pp. 289-398


1 Meliksah University, Kayseri, Turkey
2 Integrated Vehicle Health Management Centre, Cranfield University, UK
3 Lulea University of Technology, Lulea, Sweden

(Received on August 24, 2010, revised on December 18, 2011 and February 07, 2012)


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.


References: 16

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