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Track Component Failure Detection Using Data Analytics over existing STDS Track Circuit data
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本文介绍了一种基于智能轨道检测系统(STDS)的轨道电路故障识别方法,通过支持向量机(SVM)分类器对STDS电流数据进行处理,实现自动识别轨道故障,提高维护效率。

arXiv:2508.11693v1 Announce Type: cross Abstract: Track Circuits (TC) are the main signalling devices used to detect the presence of a train on a rail track. It has been used since the 19th century and nowadays there are many types depending on the technology. As a general classification, Track Circuits can be divided into 2 main groups, DC (Direct Current) and AC (Alternating Current) circuits. This work is focused on a particular AC track circuit, called "Smart Train Detection System" (STDS), designed with both high and low-frequency bands. This approach uses STDS current data applied to an SVM (support vector machine) classifier as a type of failure identifier. The main purpose of this work consists on determine automatically which is the component of the track that is failing to improve the maintenance action. Model was trained to classify 15 different failures that belong to 3 more general categories. The method was tested with field data from 10 different track circuits and validated by the STDS track circuit expert and maintainers. All use cases were correctly classified by the method.

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智能轨道检测系统 轨道电路故障 支持向量机 故障识别 维护
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