Study on Rail Profile Optimization Based on the Nonlinear Relationship between Profile and Wear Rate

Abstract

This paper proposes a rail profile optimization method that takes account of wear rate within design cycle so as to minimize rail wear at the curve in heavy haul railway and extend the service life of rail. Taking rail wear rate as the object function, the vertical coordinate of rail profile at range optimization as independent variable, and the geometric characteristics and grinding depth of rail profile as constraint conditions, the support vector machine regression theory was used to fit the nonlinear relationship between rail profile and its wear rate. Then, the profile optimization model was built. Based on the optimization principle of genetic algorithm, the profile optimization model was solved to achieve the optimal rail profile. A multibody dynamics model was used to check the dynamic performance of carriage running on optimal rail profile. The result showed that the average relative error of support vector machine regression model remained less than 10% after a number of training processes. The dynamic performance of carriage running on optimized rail profile met the requirements on safety index and stability. The wear rate of optimized profile was lower than that of standard profile by 5.8%; the allowable carrying gross weight increased by 12.7%.

Prediction Model for Liner Wear Considering the Motion Characteristics of Material

“…Last but not least, CSS also has an important influence on liner wear and can be regulated by adjusting the height of the bowl. Then liner wear for various CSS is calculated according to (18). It is clear from Figure 10  0 = 2.5 ∘  0 = 2.3 ∘  0 = 2.1 ∘  0 = 1.9 ∘  0 = 1.7 ∘  0 = 1.5 ∘  0 = 1.3 ∘  0 = 1.1 ∘ (a) = 300rpm, CSS=19mm with the findings of Asbjörnsson [10].…”

“…The wear of liner surface is mainly related to load, sliding distance, the wear resistance of the liner, and the properties of the rock material [7,17]. Considering that the process of liner wear evolves over time [18], time should also be one of the factors considered by the wear model. Therefore, a characteristic equation of wear model can be expressed as…”

“…Model versus Measurements. The surface wear of the liner can be obtained by (18) and (19). Figure 6 shows the comparison of the simulated and measured worn profiles.…”

Effects of Traffic Loads and Track Parameters on Rail Wear: A Case Study for Yenikapi–Ataturk Airport Light Rail Transit Line

“…Jeong et al [14] presented a probabilistic forecasting model for rail wear progress by using a particle filter method based on the Bayesian theory by means of rail wear data measured at the Seoul Metro. Wang et al [15] proposed a rail profile optimization method to reduce rail wear by using a support vector machine regression analysis for fitting of the nonlinear relationship between rail profile and rail wear rate. Meghoe et al [5] established relations between rail wear and railway operating conditions, including track geometry parameters, by means of metamodels obtained with regression analysis.…”

A novel prediction method for rail grinding profile based on an interval segmentation approach and accurate area integral with cubic NURBS

“…That means it solves the “What is the goal?” problem for rail grinding. There have been a number of studies on this aspect, taking the w/r contact performance 6,7 or rail’s service life as the optimization objective, 8 using different optimization algorithms to obtain the optimal grinding profile based on w/r contact theory. For instance, in order to improve the w/r contact performance and the curve negotiation ability, Zeng et al 2,9 proposed a new optimization method based on the Kriging surrogate model and the genetic algorithm to optimize the target profiles of the asymmetrical rail grinding in sharp-radius curves and the rail grinding in straight line for high-speed railway.…”