Railroad Turnout Wear Diagnostics

The article presents a few issues related to the technical condition of a railway turnout, an important element of the railway network where about 90% of railway accidents occur. In the first part of the article, the results of railway turnout wear are presented. A comparison of normal forces (in wheel–rail contact) in vehicle traffic on straight track without a turnout and normal forces occurring when a rail vehicle passes a turnout is presented. Then, turnout wear processes for selected speeds are presented. In the next part of the paper, the possibilities of using a vision system are presented, which, in combination with tools for image processing analysis, makes it possible to detect wear and distances between the key elements of a railway turnout. The main idea of the proposed online diagnostic system solution is to use the analysis of received images (photos) with the help of a vision system. The basic problem to be solved in the proposed system was to develop algorithms responsible for generating wear areas from high-resolution images. The algorithms created within the work were implemented and tested in the MATLAB software environment. The presented method is an original procedure for diagnosing turnout elements for each time instant. The proposed system is compatible with railway traffic control systems.

Possibilities of high-speed railway turnout data description

This paper aims to summarize possibilities how to create data models of high-speed railway turnouts. The turnouts designed for high-speed operation require specific geometric solution. As the UIC RailTopoModel is considered an international recommendation in the field of data modeling of railway infrastructure, this issue is assessed in terms of models based on its principles. Solving this problem can affect the future development of the Multipurpose Railway Infrastructure Model gradually emerging at the CTU Railway Laboratory in Prague using the RailTopoModel principles.Whereas the RailTopoModel itself does not define any specific types of entities, the railML® 3.1 specifications are also used for assessment purposes. Turnouts are viewed both in terms of topology and in terms of functional infrastructure. In the final sections, recommendations are given on how to deal with the problems found, e. g. in terms of implementation into the Multipurpose Railway Infrastructure Model.

An intelligent fault diagnosis method based on curve segmentation and SVM for rail transit turnout

With the development of intelligent transportation system, the maintenance of railway turnout is an essential daily task which was required to be efficiency and automatically. This paper presents an intelligent diagnosis method based on deep learning curve segmentation and the Support Vector Machine. Firstly, we studied the curve segmentation approach of the real-time monitoring power data collected form turnout, for which is an essential step and do a great help to improve the diagnose accuracy. Then based on the well pre-processed data sets, the SVM algorithm was applied to classify the samples and report the health states of the turnout which under testing. At last, the experiments were taken on the power data curve collected from the real turnouts, during which we compared the new diagnose method with conventional ones, and the results showed that the diagnose accuracy of proposed method can averaged to 98.5%. Compared with traditional SVM based frameworks, the proposed diagnosis method dramatically improves the accuracy which is more suitable for railway turnout.