To reduce maintenance costs, it is important to carry out probabilistic analyses on railway vehicle components. In this work, a data-driven approach based on a Gaussian process for regression is developed to determine the probability of axle failure caused by crack growth in railway axles. For complicated failure modes, it is difficult or even impossible to build a reliable analytical or simulation model before using an analytical approach. The main purpose of this work is to develop an algorithm to infer the distribution of crack growth from limited measured data without having to build an underlying model. The results of the case study show that the determined timing for the first inspection and the probability of failure coincide with the known results derived by analytically based approaches. The problems associated with modelling and calibration can be overcome by a data-driven approach. The developed Gaussian process model can serve as a complementary instrument to validate other analytically based approaches or numerical analyses. The model can also be applied to the probabilistic analyses of other railway components.

为了减少维护成本，对铁路车辆部件进行概率分析很重要。在这项工作中，开发了一种基于高斯过程进行回归的数据驱动方法，以确定由铁路车轴裂纹扩展引起的车轴故障的可能性。对于复杂的故障模式，在使用分析方法之前很难或什至不可能建立可靠的分析或仿真模型。这项工作的主要目的是开发一种算法，可以从有限的测量数据推断出裂纹扩展的分布，而无需建立基础模型。案例研究的结果表明，确定的首次检查时间和故障概率与基于分析的方法得出的已知结果一致。与建模和校准相关的问题可以通过数据驱动的方法来克服。所开发的高斯过程模型可以用作补充工具，以验证其他基于分析的方法或数值分析。该模型还可以应用于其他铁路组件的概率分析。