Reliability Modeling and Parameter Estimation for High-Speed Train Wheels Subject to Multi-Dimensional Degradation Processes Considering Mutual Dependency

The wheels are among the most critical components which largely influence the safe operation of high-speed trains. The existing research in reliability modeling typically assumes the wheel degradation to be a one-dimensional degradation process. This could incur a deficiency in practice, as the wheel degradation is in fact the superposition of multiple complex degradation processes, involving wheel tread wear and wheel polygonal wear. Random shocks also contribute to the wheel degradation. Moreover, these processes are correlated with each other. To fully consider these factors, this article proposes a multistate model for multidimensional degradations. The piecewise-deterministic Markov process (PDMP) model is applied to describe the mutual dependencies between random shocks and multiple degradations. Conventionally, the parameters of PDMP are set by experts’ experience. This article investigates maximum likelihood estimation to estimate the model parameters. Finally, the Monte Carlo simulation algorithm is proposed to evaluate the high-speed train wheel's reliability. Numerical experiments were conducted to validate the proposed method on high-speed train wheels subject to tread wear, polygonal wear, and wheel-rail impacts, which show that dependencies among multidimensional degradation processes and random shocks will largely affect the reliability of the wheels. The application to high-speed train wheels shows the effectiveness of the proposed model.