Rail wear and remaining life prediction using meta-models

Abstract

The study presented in this paper proposes a method to estimate the Remaining Useful Life (RUL) of railway tracks determined by wear and taking into account various track geometry and usage profile parameters. The relation between these parameters and rail wear is established by means of meta-models derived from physical models. These models are obtained with regression analysis where the best fit is found from a relatively large set of numerical experiments for various scenarios. The specific parameter settings for these scenarios are obtained by using the Latin Hypercube Sampling (LHS) method. Furthermore, for the rail profile, which is one of the input parameters for the meta-model, it is shown that the evolution due to wear in moderate curves can be characterized by only one parameter. The findings in this work including are valuable for Infrastructure Managers (IMs) and can easily be implemented in maintenance decision support tools.

Meta-model based stochastic simulation of fire barrier cold-side temperature

“…Also, the use of meta-models means additional uncertainty, i.e., one may obtain the converged solution using meta-models but not necessarily the same as the one that corresponds to the actual deterministic model. Studies presenting the use of meta-models provide Introduction the information of likelihood fraction by which the meta-model and actual model prediction differ [49,50,51,52,53,54,55,56]. Nevertheless, such information is not sufficient to explain the discrepancy in terms of output distribution essential for accurate probabilistic prediction.…”

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

“…In previous studies [1, 2, 4-8, 12, 15, 16], it has been revealed that rail wear depends greatly on vehicle speed. Influences of superelevation on rail wear have been previously emphasized [2,3,5,12]. Taking into account the findings obtained from previous studies, traffic load, track curvature, superelevation, and train speed were selected as explanatory variables for the rail wear models proposed in the present study.…”

“…Rail wear depends on various parameters such as the axle load, train speed, profiles of wheel and rail, material properties of wheel and rail, track curvature, traffic type, condition of the wheel-rail contact surface, contact pressure, lubrication, and environmental effects [1,4]. Rail wear causes the location change of the contact points between wheel and rail, leading to deterioration of the wheel-rail contact geometry and instability of railway vehicles [5]. Material loss due to wear results in a significant decrease in motion stability and ride comfort, with an increased risk of derailment of trains.…”

“…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.…”

Overview of Sensitivity Analysis Methods Capabilities for Traction AC Machines in Electrified Vehicles

“…In fact, most of the HEV/EV powertrain models are nonlinear which raises the following question, "What would be the consequences if the SA results are incorrect?" On the other hand, global sensitivity analyses (GSA) [35][36][37][38][39][40][41][42][43][44][45], which avoids the aforementioned shortcomings, are only employed by a minority of designers. From the literature published on SA-focused studies for different HEV [46][47][48] and EV [50][51][52] powertrain topologies in both commercial [51][52][53] and passenger [54][55] vehicles, a remarkable lack of the availability of data can be seen.…”