基于动态机器学习的车载轮缘磨损监测系统，精度达98.2%。
Advancing rail safety: An onboard measurement system of rolling stock wheel flange wear based on dynamic machine learning algorithms
- 用位移与温度传感器数据，动态训练回归模型监测磨损。
- 系统精度达96.5%，结合滤波后提升至98.2%。
- 适合铁路运维人员实时掌握轮轨状态，保障行车安全。
铁路车辆轮轨相互作用对系统安全至关重要，需精确测量以实现有效监控。本文提出一种基于位移和温度传感器的车载轮缘磨损深度监测系统。通过实验室模拟不同时间段的轮缘磨损深度及环境温度变化，采集数据并动态自动化训练基于回归模型的机器学习算法。进一步采用标准流程验证系统有效性。为提高精度，设计了一种无限冲激响应滤波器（IIR），用于抑制车辆动力学与传感器噪声，其参数基于机车仿真与实验数据的快速傅里叶变换分析确定。结果表明，该动态机器学习算法能有效抵消温度对传感器非线性响应的影响，准确率达96.5%，且运行时间极短；结合IIR滤波后，精度进一步提升至98.2%。该系统可集成于铁路物联网通信嵌入式系统，实时提供轮缘磨损与轨道不平顺状况的洞察，显著提升铁路运营的安全性与效率。
原文摘要 · Abstract (English)
Rail and wheel interaction functionality is pivotal to the railway system safety, requiring accurate measurement systems for optimal safety monitoring operation. This paper introduces an innovative onboard measurement system for monitoring wheel flange wear depth, utilizing displacement and temperature sensors. Laboratory experiments are conducted to emulate wheel flange wear depth and surrounding temperature fluctuations in different periods of time. Employing collected data, the training of machine learning algorithms that are based on regression models, is dynamically automated. Further experimentation results, using standards procedures, validate the system's efficacy. To enhance accuracy, an infinite impulse response filter (IIR) that mitigates vehicle dynamics and sensor noise is designed. Filter parameters were computed based on specifications derived from a Fast Fourier Transform analysis of locomotive simulations and emulation experiments data. The results show that the dynamic machine learning algorithm effectively counter sensor nonlinear response to temperature effects, achieving an accuracy of 96.5 %, with a minimal runtime. The real-time noise reduction via IIR filter enhances the accuracy up to 98.2 %. Integrated with railway communication embedded systems such as Internet of Things devices, this advanced monitoring system offers unparalleled real-time insights into wheel flange wear and track irregular conditions that cause it, ensuring heightened safety and efficiency in railway systems operations.
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