This paper presents a hybrid method to assess the rail health with the focus on a specific type of rail surface defect called head check. The proposed method uses physics-based and data-driven models in order to model defect initiation and defect evolution on a rail for a given rail traffic tonnage. Ultrasonic (US) and Eddy Current (EC) defect detection measurements are used to provide Infrastructure Managers (IMs) with insight in the current rail condition. The defect initiation results obtained from the first part of the hybrid method which consists of the physics-based model is successfully validated with the EC measurements. Furthermore, the US and EC measurements are utilized to derive a data-driven model for defect evolution. Finally, a set of robust and predictive Key Performance Indicators (KPIs) are proposed to quantify the future condition of the rail based on different characteristics of rail health resulting from the defect initiation and defect evolution analysis.

本文提出了一种用于评估铁路运行状况的混合方法，重点是一种称为“头部检查”的特殊类型的铁路表面缺陷。所提出的方法使用基于物理和数据驱动的模型，以便对给定的铁路运输吨位上的缺陷引发和缺陷演化进行建模。超声波（US）和涡流（EC）缺陷检测测量用于为基础架构管理器（IM）提供有关当前铁路状况的洞察力。从混合方法的第一部分（由基于物理的模型组成）获得的缺陷引发结果已通过EC测量成功验证。此外，US和EC测量被用来导出数据驱动的模型以进行缺陷演化。最后，