Automatic air brake systems are widely used in freight train braking to ensure railway operation safety. Various types faults pose an enormous threat to freight operations. Existing algorithms lack a unified framework for generating key features. In this research, we propose a novel feature engineering framework for the fault diagnosis of freight train air brakes. First, experimental data are collected through a three-car in-lab experimental platform. Second, a peak detection method combined with first-order difference function to partition and classify the air pressure time series into the braking phase and releasing phase. Third, a divided-and-integrated framework is designed for feature engineering. Feature selection is carried out via a modified reinforcement learning method. Finally, multiple machine learning algorithms are explored and the results indicate that random forest method shows the best performance. The proposed model achieves about 99% accuracy for car-level fault detection and over 94% accuracy for component-level fault diagnosis.

自动空气制动系统广泛用于货运列车制动，以确保铁路运营安全。各类故障对货运业务构成巨大威胁。现有算法缺乏用于生成关键特征的统一框架。在这项研究中，我们提出了一种新的特征工程框架，用于货运列车空气制动器的故障诊断。首先，通过三车实验室实验平台收集实验数据。其次，结合一阶差分函数的峰值检测方法将气压时间序列划分和分类为制动阶段和释放阶段。第三，为特征工程设计了一个分而合一的框架。特征选择是通过改进的强化学习方法进行的。最后，探索了多种机器学习算法，结果表明随机森林方法表现出最佳性能。所提出的模型对汽车级故障检测的准确率达到了约 99%，对组件级故障诊断的准确率超过了 94%。