Fault detection in railway point drive supported by data mining methods

Abstract

In this work diagnostics of railway point drive supported by Data Mining methods was considered. Results of FEM calculations of switching forces acting on the considered point are qualitatively correct, so Data Mining methods efficiency was examined on data obtained from FEM multi-body model. Hidden structures in data and patterns describing particular faults were identified. Proposed algorithms of Kohonen's neural networks and k-means clustering are easy to apply to classifying. Their implementation on the Digital Signal Processor is not difficult and memory consumption is low so diagnostic module supported by implemented Data Mining methods was proposed in order to preliminary asses technical state of railway points and to assure current state monitoring and supporting maintenance activities.