Simulation of wheel and rail profile wear: a review of numerical models

The development of numerical models able to compute the wheel and rail profile wear is essential to improve the scheduling of maintenance operations required to restore the original profile shapes. This work surveys the main numerical models in the literature for the evaluation of the uniform wear of wheel and rail profiles. The standard structure of these tools includes a multibody simulation of the wheel-track coupled dynamics and a wear module implementing an experimental wear law. Therefore, the models are classified according to the strategy adopted for the worn profile update, ranging from models performing a single computation to models based on an online communication between the dynamic and wear modules. Nevertheless, the most common strategy nowadays relies on an iteration of dynamic simulations in which the profiles are left unchanged, with co-simulation techniques often adopted to increase the computational performances. Work is still needed to improve the accuracy of the current models. New experimental campaigns should be carried out to obtain refined wear coefficients and models, while strategies for the evaluation of both longitudinal and transversal wear, also considering the effects of tread braking, should be implemented to obtain accurate damage models

Statistical Modelling of Wear and Damage Trajectories of Railway Wheelsets

This paper discusses the use of Linear Mixed Models (LMM) and Generalized Linear Mixed Models (GLMM) to predict the wear and damage trajectories of railway wheelsets for a fleet of modern multiple unit trains. The wear trajectory is described by the evolution of the wheel flange thickness, the flange height and the tread diameter; whereas the damage trajectory is assessed through the probabilities of various types of wheel tread damage such as rolling contact fatigue, wheel flats and cavities occurring. Different model specifications are compared based on an information criterion

Can the whole life cost of railway track be reduced through the effective management of tangential wheel-rail loading?

Imperial Users onl

Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities

Contemporary Inspection and Monitoring for High-Speed Rail System

Non-destructive testing (NDT) techniques have been explored and extensively utilised to help maintaining safety operation and improving ride comfort of the rail system. As an ascension of NDT techniques, the structural health monitoring (SHM) brings a new era of real-time condition assessment of rail system without interrupting train service, which is significantly meaningful to high-speed rail (HSR). This chapter first gives a review of NDT techniques of wheels and rails, followed by the recent applications of SHM on HSR enabled by a combination of advanced sensing technologies using optical fibre, piezoelectric and other smart sensors for on-board and online monitoring of the railway system from vehicles to rail infrastructure. An introduction of research frontier and development direction of SHM on HSR is provided subsequently concerning both sensing accuracy and efficiency, through cutting-edge data-driven analytic studies embracing such as wireless sensing and compressive sensing, which answer for the big data’s call brought by the new age of this transport

Data-Driven Operational and Safety Analysis of Emerging Shared Electric Scooter Systems

The rapid rise of shared electric scooter (E-Scooter) systems offers many urban areas a new micro-mobility solution. The portable and flexible characteristics have made E-Scooters a competitive mode for short-distance trips. Compared to other modes such as bikes, E-Scooters allow riders to freely ride on different facilities such as streets, sidewalks, and bike lanes. However, sharing lanes with vehicles and other users tends to cause safety issues for riding E-Scooters. Conventional methods are often not applicable for analyzing such safety issues because well-archived historical crash records are not commonly available for emerging E-Scooters. Perceiving the growth of such a micro-mobility mode, this study aimed to investigate E-Scooter operations and safety by collecting, processing, and mining various unconventional data sources. First, origin-destination (OD) data were collected for E-Scooters to analyze how E-Scooters have been used in urban areas. The key factors that drive users to choose E-Scooters over other options (i.e., shared bikes and taxis) were identified. Concerning user safety tied to the growing usage, we further assessed E-Scooter user guidelines in urban areas in the U.S. Scoring models have been developed for evaluating the adopted guidelines. It was found that the areas with E-Scooter systems have notable disparities in terms of the safety factors considered in the guidelines. Built upon the usage and policy analyses, this study also creatively collected news reports as an alternative data source for E-Scooter safety analysis. Three-year news reports were collected for E-Scooter-involved crashes in the U.S. The identified reports are typical crash events with great media impact. Many detailed variables such as location, time, riders’ information, and crash type were mined. This offers a lens to highlight the macro-level crash issues confronted with E-Scooters. Besides the macro-level safety analysis, we also conducted micro-level analysis of E-Scooter riding risk. An all-in-one mobile sensing system has been developed using the Raspberry Pi platform with multiple sensors including GPS, LiDAR, and motion trackers. Naturalistic riding data such as vibration, speed, and location were collected simultaneously when riding E-Scooters. Such mobile sensing technologies have been shown as an innovative way to help gather valuable data for quantifying riding risk. A demonstration on expanding the mobile sensing technologies was conducted to analyze the impact of wheel size and riding infrastructure on E-Scooter riding experience. The quantitative analysis framework proposed in this study can be further extended for evaluating the quality of road infrastructure, which will be helpful for understanding the readiness of infrastructure for supporting the safe use of micro-mobility systems. To sum up, this study contributes to the literature in several distinct ways. First, it has developed mode choice models for revealing the use of E-Scooters among other existing competitive modes for connecting urban metro systems. Second, it has systematically assessed existing E-Scooter user guidelines in the U.S. Moreover, it demonstrated the use of surrogate data sources (e.g., news reports) to assist safety studies in cases where there is no available crash data. Last but not least, it developed the mobile sensing system and evaluation framework for enabling naturalistic riding data collection and risk assessment, which helps evaluate riding behavior and infrastructure performance for supporting micro-mobility systems

Prediction of Rail Damage on Underground-Metro Lines

Safety and reliability of rails primarily depend on detection, monitoring and maintenance of rolling contact fatigue (RCF) defects. Since when they are undetected and untreated, they can further propagate and increase the risk of rail failures. Thereby, infrastructure managers (IMs) tend to detect these cracks at an early stage in order to manage this risk. After detection, the growth of these cracks should be monitored and efficient maintenance should be carried out to prolong the rail life. However, this requires reliable and sufficient field data with accurate prediction models of RCF damage and its counterpart damage mechanism; wear. Although the current models, which are particularly used on real track conditions, focus on mainline routes and were often validated using rail surface observations, lesser emphasis has been placed on underground-metro system tracks and the use of non-destructive testing (NDT) measurements particularly the crack depth which is a key parameter in the assessment of crack severity and maintenance planning. In recent years, London Underground (LUL) has put additional effort to improve their rail inspection practices to support the optimisation of their rail maintenance strategy. Besides the use of several different NDT techniques, the magnetic flux leakage based sensor is used to measure the depth of detected cracks. Research suggested that this rail inspection data could be used to improve the accuracy of damage predictions. With the help of successive measurements, the severity of damage could be quantified and the changes in RCF estimations and its interaction with wear over time could be demonstrated. It was proposed that this should increase the confidence in prediction models for maintenance planning and help to support future maintenance optimisation. Owing to use of different NDT techniques, a significant volume of field defect data was collected and examined in detailed during the research to understand the dominant damage mechanisms and the influential factors promoting RCF crack growth. It was found that severe damage is not limited to mainline and freight routes, with rails on metro lines also suffering from a high number of cracks. In addition, the various track data consisting of wheel-rail profile measurements, track geometry, vehicle speed diagrams and traffic information were also submitted. This provided a good opportunity to build detailed vehicle dynamics simulations for the selected lines to be further studied on LUL. The Whole Life Rail Model (WLRM) and the Shakedown Map were selected as predictive models since, they can integrate with vehicle dynamics simulations. However, when the main input of WLRM; ‘Tγ’ was initially applied to LUL tracks, it was found that while, it successfully showed the effect of significant factors, it resulted in over-and under-estimation of the RCF damage in several locations. Therefore, the research investigated the interaction between the model parameters and their comparison on sites with and without reported RCF defects to find an optimum solution. The results indicated certain distinctions and hence, new wear and RCF damage prediction methods were developed using a combined Shakedown Map and Tγ approach. Both of the methods were applied to selected LUL RCF monitoring sites. Whilst the wear method was applied to predict the loss in cross-section area of the rail, the new RCF crack depth prediction model was validated using the MRX-RSCM crack depth measurements. The location and severity of both damage types were successfully predicted. To observe and predict the changes in RCF damage including the interaction of wear over successive measurement intervals brought novelty to the study. In addition, the accuracy of predictions was improved on sites with various track characteristics such as high and low rail of checked and unchecked curved track section and tangent tracks