Abstract Wear modelling of train wheels has long been an important research topic; both physics-based and data-based methods have some weaknesses. To bridge the gap between them and take their advantages, this paper proposes a new physics-based data-driven guideline for wheel wear modelling and prediction. First, based on wear mechanism analysis, the basic model of tread wear and flange wear are designed considering their correlation; wear models are established separately for different wheel positions considering the uncertainty in a wear process, and further trained mathematically with wear data. Second, wheel reprofiling is closely related to wheel wear and is modelled based on theoretical analysis and reprofiling data. Then, the numerical method for predicting wheel degradation is proposed based on the closed-loop alternation between wear and reprofiling; the remaining useful life (RUL) of wheels is further evaluated through point estimation and interval estimation. Finally, the good agreement between trained models and wear data validates the wear models; the proposed guideline is verified by measurement data to produce accurate prediction of wheel degradation and effective evaluation of wheel RUL. The proposed guideline has been applied to the prognostics and health management of wheels for various train types.

中文翻译：

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一种新的基于物理的数据驱动的列车车轮磨损建模和预测指南

摘要 列车车轮磨损建模长期以来一直是一个重要的研究课题；基于物理和基于数据的方法都有一些弱点。为了弥补它们之间的差距并利用它们的优势，本文提出了一种新的基于物理的数据驱动的车轮磨损建模和预测指南。首先，在磨损机理分析的基础上，设计了胎面磨损和轮缘磨损的基本模型，考虑了两者的相关性；考虑到磨损过程中的不确定性，针对不同的车轮位置分别建立磨损模型，并使用磨损数据进行进一步的数学训练。其次，车轮修整与车轮磨损密切相关，并根据理论分析和修整数据进行建模。然后，提出了基于磨损和重塑之间的闭环交替预测车轮退化的数值方法；通过点估计和区间估计进一步评估车轮的剩余使用寿命（RUL）。最后，经过训练的模型和磨损数据之间的良好一致性验证了磨损模型；建议的指南通过测量数据进行验证，以准确预测车轮退化并有效评估车轮 RUL。拟议的指南已应用于各种列车类型的车轮的预测和健康管理。建议的指南通过测量数据进行验证，以准确预测车轮退化并有效评估车轮 RUL。拟议的指南已应用于各种列车类型的车轮的预测和健康管理。建议的指南通过测量数据进行验证，以准确预测车轮退化并有效评估车轮 RUL。拟议的指南已应用于各种列车类型的车轮的预测和健康管理。