Personalized grinding strategy for mitigating carbody vibration in worn turnout

Abstract

When trains pass through turnouts, the lateral vibration acceleration of the carbody (LVAC) often exceeds the permissible limit, with excessive wheel-rail wear being one of the causes of this phenomenon. Rail grinding is considered a potential solution, but there is currently a lack of a grinding strategy model to provide precise grinding strategies in engineering. To address this issue, wheel profiles associated with excessive LVAC and their corresponding turnout rail profiles are collected and analyzed. The worn rail profiles are fitted using cubic Non-uniform Rational B-splines (NURBS) curves based on value points. Based on the vehicle dynamics model and the Back Propagation Neural Network-based Non-dominated Sorting Genetic Algorithm II (BP-NSGA II), the optimized profiles of personalized grinding target are obtained. By integrating considerations of grinding efficiency and quality, the grinding modes and strategies are optimized, ultimately leading to the development of a personalized grinding strategy model (PGSM). Research results show that the optimized profiles effectively reduce the grinding depth and improve the wheel-rail contact interaction. The PGSM can calculate optimized grinding strategies in real-time for practical engineering. After applying this model, significant improvements are observed in LVAC, derailment coefficient, and wear index. In summary, the PGSM offer a robust solution for enhancing rail grinding efficiency and mitigating carbody vibration, thereby contributing to improved railway performance and safety.