Failure time prediction for vehicle dynamics under performance degradation of dampers and track evolution

During long-term service, railway vehicles will experience damper performance degradation and track evolution, both have significant effects on the vehicle dynamics performance. This study aims to develop a mathematical model for assessing the failure time of the vehicle system dynamics when considering two random factors. As for the methodology, Wiener processes are used to propose a damper degradation model. Moreover, a continuous-time Markov chain is adopted to describe the state transaction of the track irregularities. In addition, the Monte Carlo method is employed to sample the dynamic responses of the vehicle using stochastic parameters. Finally, the Bayesian theory is utilized to establish the reliability model of the Weibull mixtures under the Dirichlet process prior. The results are obtained through a numerical example: the dampers’ individual differences directly affect effect on the rate of degradation of the vehicle dynamics performance. Furthermore, different indexes positively correlate with each other, mixture models are composed of a small number of components, and Weibull mixtures closely approximate the distribution of the failure time. Compared to other dampers, the anti-yaw dampers have a more significant impact on the vehicle system dynamics. Other results show that each type of damper can adopt a uniform replacement period whereas anti-yaw dampers should be more effectively monitored.