SVM Based Diagnostics on Railway Turnouts

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. [PUBLICATION ABSTRACT]