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.

The current research was aimed to investigate atmospheric corrosion behaviors of 6005A and 6082 aluminum alloys for a certain application in high-speed railway employed for service in Thailand. Actual exposure atmospheric test with the maximum period of 18 months was conducted at urban and marine–coastal environments. After completion of actual exposure test, corrosion behaviors of the uncoated alloys were determined based on corrosion mass loss and pitting corrosion aspects. It turned out that remarkable corrosion severity found at marine–coastal environment with respect to urban environment was attributed to higher deposition rates of cumulative chloride (around 1331 mg m−2 day−1) and sulfur dioxide (around 200 mg m−2 day−1) together with higher levels of RH (>80%) for the entire year of exposure. The alloys exposed at marine–coastal environment for 18 months long revealed the corrosion mass loss of approximately 2 g m−2, average pit depth of greater than 80 μm, and density of around 3 pits⋅cm−2.