An Optimal Reprofiling Policy for High-Speed Train Wheels Subject to Wear and External Shocks Using a Semi-Markov Decision Process

作者：E, Mingcheng^*; Li, Bing; Jiang, Zengqiang; Li, Qi

来源：IEEE Transactions on Reliability, 2018, 67(4): 1468-1481.

DOI：10.1109/TR.2018.2870276

• External shocks
• policy-iteration algorithm
• reprofiling policy
• semi-Markov decision process (SMDP)
• wear
• wheels

Wheels are one of the three components of rail vehicles that are most affected by wear, having significant implications on safety and comfort. For the safety of vehicles, maintenance activities are needed for worn wheels to ensure their normal geometry, where reprofiling plays the most important role in maintenance. How to reprofile wheels scientifically and economically has become a vital issue, which needs to be solved in the field of wheel maintenance. This study focuses on high-speed train wheels and proposes a dynamic reprofiling policy based on condition monitoring at equidistant discrete time inspections. The problem is modeled in the framework of a semi-Markov decision process considering both wear and external shocks. The objective of the maintenance model is to minimize the long-term expected cost per unit time. We apply a policy-iteration algorithm to determine the optimal reprofiling policy. To demonstrate the model's effectiveness, actual degradation data on high-speed train wheels are used as an illustration. The results show that the dynamic reprofiling policy reduces wheel maintenance costs and extends their service life, also ensuring wheels' safety.

• 出版日期2018-12
• 单位北京交通大学