【摘要】 目前，25 Hz相敏轨道电路的故障排查主要依赖人工，无法实现快速精准的故障定位，为此，开展基于逻辑推理和集成学习的25 Hz相敏轨道电路的故障诊断研究。根据轨道电路的工作原理，搭建轨道电路模型，并通过信号传输内在逻辑推理分析，建立常见故障模式下的电压信号监测方案；采用AdaBoost集成学习算法构建故障诊断模型，并通过在轨道电路模型中注入故障，构建对应的故障模型以获取故障数据集；最后在实际电路环境中模拟故障并验证故障诊断模型的性能。验证结果表明，该模型能够比通用模型更准确地诊断轨道电路故障。更多还原

【Abstract】 Due to the fact that the troubleshooting of 25 Hz phase sensitive track circuits mainly relied on manual labor and could not implement fast and accurate fault location, this paper conducted research on fault diagnosis of 25 Hz phase sensitive track circuits based on logical reasoning and integrated learning. It built a track circuit model based on the working principle of the track circuit, and established a voltage signal monitoring scheme under common fault modes through internal logical reasoning analysis of signal transmission, used AdaBoost ensemble learning algorithm to construct a fault diagnosis model, and injected faults into the track circuit model to construct the corresponding fault model and obtain a fault dataset. Finally, it simulated faults in actual circuit environments and verified the performance of the fault diagnosis model. The verification results indicate that the model can diagnose track circuit faults more accurately than the general model.更多还原