An automatic train protection (ATP) system is the core to ensure operation safety of high-speed railway. At present, failure rate change rules of the system are not well understood and the maintenance strategy is not refined. In order to improve the protection capability and maintenance level of high-speed trains, this paper proposes a decision tree machine learning model for failure feature extraction of ATP systems. First, system type, mean operation mileage, mean service time, etc. are selected as ATP failure feature parameters, and cumulative failure rate is selected as a model output label. Second, support vector machine, AdaBoost, artificial neural networks and decision tree model are adopted to train and test practical failure data. Performance analysis shows that decision tree learning model has better generalization ability. The accuracy of 0.9761 is significantly greater than the other machine learning models. Therefore, it is most suitable for failure features analysis. Third, interpretability analysis reveals the quantitative relationship between system failure and features. Finally, an intelligent maintenance system for ATP systems is built, which realize the refined maintenance throughout life cycle.

中文翻译：

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高速列车自动保护系统故障特征分析

列车自动保护（ATP）系统是保障高铁运行安全的核心。目前对系统的故障率变化规律还不是很了解，维护策略也没有细化。为提高高速列车的保护能力和维修水平，提出了一种用于ATP系统故障特征提取的决策树机器学习模型。首先选择系统类型、平均运行里程、平均服务时间等作为ATP故障特征参数，选择累计故障率作为模型输出标签。其次，采用支持向量机、AdaBoost、人工神经网络和决策树模型对实际失效数据进行训练和测试。性能分析表明决策树学习模型具有较好的泛化能力。0.9761 的准确率明显高于其他机器学习模型。因此，它最适合于故障特征分析。第三，可解释性分析揭示了系统故障与特征之间的定量关系。最后构建了ATP系统智能维护系统，实现了全生命周期的精细化维护。