用智能算法从轨道电路数据中自动识别故障部件，提升铁路维护效率
Track Component Failure Detection Using Data Analytics over existing STDS Track Circuit data
- 基于STDS电流数据，用SVM分类器识别15种故障
- 在10个轨道电路实测数据上实现100%准确分类
- 适合铁路信号系统维护人员快速定位硬件故障
轨道电路（TC）是检测列车位置的主要信号装置，自19世纪以来广泛使用，现有多类技术，主要分为直流（DC）和交流（AC）两类。本文聚焦于一种特定的交流轨道电路——智能列车检测系统（STDS），该系统采用高低频双频段设计。通过分析STDS电流数据，结合支持向量机（SVM）分类器，构建了一种故障识别方法。目标是自动判断轨道中具体哪个组件发生故障，以优化维护操作。模型训练用于区分15种不同故障，归为3个更广泛的类别。方法在来自10个不同轨道电路的真实现场数据上进行测试，并由STDS专家及维护人员验证，所有案例均被正确分类。
原文摘要 · Abstract (English)
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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