Wheel Wear Research Articles

Experimental and Numerical Investigation of Bogie Hunting Instability for Railway Vehicles Based on Multiple Sensors.

Bogie hunting instability is one of the common faults in railway vehicles. It not only affects ride comfort but also threatens operational safety. Due to the lower operating speed of metro vehicles, their bogie hunting stability is often overlooked. However, as wheel tread wear increases, metro vehicles with high conicity wheel-rail contact can also experience bogie hunting instability. In order to enhance the operational safety of metro vehicles, this paper conducts field tests and simulation calculations to study the bogie hunting instability behavior of metro vehicles and proposes corresponding solutions from the perspective of wheel-rail contact relationships. Acceleration and displacement sensors are installed on metro vehicles to collect data, which are processed in real time in 2 s intervals. The lateral acceleration of the frame is analyzed to determine if bogie hunting instability has occurred. Based on calculated safety indicators, it is determined whether deceleration is necessary to ensure the safety of vehicle operation. For metro vehicles in the later stages of wheel wear (after 300,000 km), the stability of their bogies should be monitored in real time. To improve the stability of metro vehicle bogies while ensuring the longevity of wheelsets, metro vehicle wheel treads should be reprofiled regularly, with a recommended reprofiling interval of 350,000 km.

Challenges and Issues in Implementing & Operationalising Big Data Analytics Capabilities in a major Australian Railway Organisation: A Case Study

This study examines the implementation of a Big Data Analytics (BDA) project within a major Australian freight and railway organisation. It also identifies the issues and challenges with data collection, data cleaning, data modelling, and data science software, and implements these models to deliver tangible business results. In addition, the project highlights the potential gains that a data analytics project, integrated with a data-driven culture, can provide through significant operational efficiencies and financial gains. Prior to 2019, the company had little exposure to Predictive Analytics. This study shows how the development of data science capability enables the creation of advanced predictive models, particularly in this case study, for the prediction of train wheel wear, and therefore a significant reduction in maintenance expenses Furthermore, a Data Analytics Maturity Assessment was conducted to determine the requirements to become a data-driven organisation. The outcome of the assessment was compared to recent global studies, and it was found that the organisation examined was significantly behind its counterparts in the areas of resources and analytic capabilities, and therefore required investment in these areas. Further studies to examine the degree of Data Analytics maturity within the Australian context are suggested. Organisations striving to become more data-driven need to plan and allocate resources for capability development in infrastructure, data management, employee quantitative skills, and governance.

Research on the preparation and road performance of modified epoxy resin chip seal

Abstract In order to explore the road performance of modified epoxy resin on cement concrete surfaces, based on the modified epoxy resin and flexible curing agent, the road performance of modified epoxy resin chip seal with different pavement structures and binder dosage was studied. When using the premixed structure with 3 to 5 mm gravel, the friction coefficient value of the chip seal was 48, the 7d wet wheel wear value was 254.5 g/m2. When using the layered structure with 3 to 5 mm gravel, the friction coefficient value of the chip seal was 84. The bond strength between the modified epoxy chip seal and the cement concrete surface was 3.13 MPa using the layered structure. The research results showed that the road performance of the layered structure chip seal was better than that of the premixed structure chip seal. The chip seal with a layered structure was more suitable for the preventive maintenance of cement concrete pavement.

Study on the generation mechanism of curve squeal and its relationship with wheel/rail wear

This paper aims to create a friction coupling finite element model of the wheelset–track system, taking into account various curve radii and corresponding wheel/rail contact states. This model will serve as a tool to analyze the underlying causes of curve squeal phenomena. Furthermore, we conducted an investigation into the impact of rail and wheel wear on system stability. This paper utilized a complex eigenvalue analysis (CEA) method to accurately calculate and predict vibration instabilities in the system. The results show that the smaller the radius of the curve, the more prone to it is lateral creep between the rail and the leading wheelset, so that the system is more prone to self-excited frictional vibrations, ultimately resulting in the generation of curve squeal. In addition, the wear of the wheel tread/rail head increases the instability of the system. When the train passes through a section with rail corrugation, rail corrugation suppresses the occurrence of curve squeal.

Development of a wheel wear prediction model considering the interaction of abrasive block–wheel and wheel–rail

A reliable model for predicting wheel wear that considers the interaction between an abrasive block and a wheel as well as the contact between the wheel and rail is developed. First, a wheel wear prediction model that integrates vehicle system dynamics and the Archard wear model for wheel–rail interactions is developed. The wheel wear prediction procedure is validated using measured wheel profiles without the influence of abrasive blocks. Second, a new wheel wear prediction model that integrates the previously validated wheel wear prediction model, a finite element method, and a modified Archard wear model for abrasive block–wheel interactions is developed. The new method for predicting wheel wear is validated using the measured data with acting abrasive blocks. The results show that the tread wear rate due to wheel–rail contact is approximately 0.057 mm/104 km and that the tread wear rate due to abrasive block–wheel and wheel–rail contact is approximately 0.070 mm/104 km at a continuous air pressure of 3.0 bar.

Hunting stability control of high-speed bogie based on active yaw damper

Hunting stability is an inherent property of railway vehicles that determines the operational speed. This paper establishes a half-vehicle model with nonlinear wheel/rail equivalent conicity and interactive forces. Additionally, the dynamic performances of vehicle under various levels of wheel wear with passive suspension are compared and analyzed. Importantly, the investigation delves into the hunting stability of the vehicle system, employing both linear and nonlinear control approaches. The results demonstrate a notable reduction in critical speed during the end-worn period with a passive suspension. However, this reduction can be substantially countered through the application of active control, resulting in a significant speed increase. The implementation of stiffness control raises the frequency of limit cycles, whereas damping control serves to diminish it. Notably, an appropriate linear cubic stiffness control effectively mitigates the amplitudes of limit cycles during instances of instability. Moreover, the control strategy derived from the simplified model is extended to enhance the stability of the entire vehicle system. The research findings hold the potential to offer a promising strategy for the active control of high-speed vehicles, particularly during periods of wheel wear.

Precision forecasting of grinding wheel Wear: A TransBiGRU model for advanced industrial predictive maintenance

In intelligent manufacturing, accurate prediction of grinding wheel wear is essential to reduce maintenance costs and improve production efficiency. To achieve accurate forecasts, this paper introduces a grinding wheel wear prediction model, TransBiGRU, incorporating a Transformer encoder, Bidirectional Gated Recurrent Unit (BiGRU), positional encoding layer, and position-wise feedforward layer. The model extracts features by analyzing current signal characteristics in basic statistical, time, and frequency domains during the machining process. Training is conducted through K-fold cross-validation to ensure model stability. The experimental results indicate that the model achieved good performance with Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2) evaluation metrics, obtaining values of 2.0898, 3.262, and 0.9338, respectively. Systematically reducing modules validates the importance of each module in enhancing predictive capabilities. This research achieves the practical application of low-cost current sensors in optimizing maintenance plans and reducing downtime in predicting grinding wheel wear.

Investigation of curve minimum radius in heavy-haul railway to improve wheel wear evolution and wheel-rail contact geometry

To determine the minimum and reasonable radius of horizontal curve for reducing wheel wear in heavy-haul railway design, a HXD locomotive with the wheel arrangement of C0-C0 is taken as the research object. Both the corresponding locomotive dynamic model and wheel wear model are established. Based on the measured wear data in a specific line, the simulation model is verified with the prediction error of wheel wear depth below 0.5 mm in general. The wheel wear evolution rules in 400∼1000m radius curves are studied and compared. The results indicate that, the wheel wear is large and sensitive to curve radius within 400∼600 m. The larger radius curve will be helpful to reduce wheel flange contact and wheel wear. For curve radius increasing from 600m to 1000m, the wheel average wear reduces slowly. Especially for curve radius below around 740 m, the wheel flange wear will be predominant. For radius above 740m, the wheel tread wear becomes the main wear form and the wheel flange wear can be reduced significantly. The increase of curve radius will also improve the wheel-rail equivalent conicity. If the minimum curve radius is set as 800m, the equivalent conicity difference between new wheelset and 1st ∼ 3rd worn wheelset can be kept below 21%, 52% and 9% respectively. In this case, the equivalent conicity is closest to the initial state and reaches a steady state, which will not change significantly with the radius further increasing. Viewed from the aspects of reducing wheel wear, avoiding wheel flange contact and keeping conicity stable simultaneously, the minimum radius of horizontal curve should be limited to 800 m. The research results validate the minimum curve radius suggested in Code for Design of Heavy-haul Railway in China, and provide a scientific explanation for the determination of this value.

Research on long term tracking of centralized power EMU wheel wear and vibration performance

The centralized power EMU is a new type of passenger transportation equipment for ordinary-speed railways in China, which can greatly improve the efficiency of ordinary-speed railway passenger transportation and improve passenger travel conditions. This paper conducts long-term dynamic tracking experiments on this new type of railway passenger car, studying the evolution laws of wheel wear, axle box vibration, bogie frame vibration, and car body vibration during long-term service, and analyzing typical vehicle dynamics phenomena that occur in long-term tracking experiments. Various studies have shown that the vehicle can maintain good lateral stationarity throughout the entire wheel reprofile cycle, and the growth rate of equivalent conicity gradually slows down as the mileage increases; However, when the mileage after wheel reprofiling is large, harmonic vibration may occur in individual sections of the frame, leading to abnormal vibration of the control car body at 10 Hz. The vertical vibration of the axle box will significantly increase at the rail joint, and its 70 Hz vibration is transmitted to the frame through the experiment suspension, stimulating the bending mode of the crossbeam of the frame, resulting in a more obvious 70 Hz vibration in the vertical direction of the frame. The RMS of the axle box, frame, and body sleeper does not show significant changes with the rise in mileage following wheel reprofiling.

Tracking of car body vibration performance of centralized power EMU

The centralized power EMU is a new type of passenger transport vehicle in the process of upgrading the transportation equipment of China’s ordinary speed railways. It can significantly improve the transportation efficiency and passenger comfort of ordinary-speed railways. Detailed research on its dynamic performance during service can guide the widespread use and maintenance of this type of EMU. This paper conducted long-term tracking tests on a centralized power EMU. It analyzes the dynamic performance and evolution rule of the centralized power EMU during long-term service from the perspectives of wheel wear and car body vibration. It compares the performance differences between the control and the trailer car. In the tracking experiment, it was found that the car body exhibits vertical low-frequency vibration. Many experimental studies have shown that the frequency at which the vehicle passes through a bridge that stretches across a distance of 32 meters on the bridge pier at 160 km/h is 1.3 Hz, which excites the nodding mode and causes significant vertical harmonic vibration of the car body.

Multi-objective optimisation of wheel-rail adhesion for high-speed trains with aerodynamic lift wings

A high-speed train with aerodynamic lift wings (aero-wings) is designed to improve train speed and mitigate energy consumption. Nevertheless, the impact of aero-wings on wheel-rail adhesion and safety remains uncertain. In this examination, a multi-body dynamics (MBD) model of a high-speed train integrated with aero-wings was first created. The influences of train speed, wheel-rail contact conditions, and aerodynamic lift on the wheel-rail adhesion, wheel wear, and dynamics performance were briefly investigated, indicating that aerodynamic lift degrades wheel-rail adhesion and dynamics performance but reduced wheel wear. Then, the multi-parameters matching optimisation design for sufficient wheel-rail adhesion and minimal wheel wear was conducted. The results indicate that, under dry contact and traction conditions, the minimum wear and optimal wheel-rail adhesion can be achieved when the train speed is about 440.62 km/h, the aerodynamic lift is about 24.48% of the vehicle weight, and the wheel wear optimisation rate is 25.38%. While the train speed of 549.54 km/h and an aerodynamic lift of 12.39% corresponds to the optimal results with wear reduction of 11.62%, under wet and braking conditions. This study provides valuable insights for determining aerodynamic load limits and formulating traction/braking control strategies for high-speed trains with aero-wings.

A Method for Identifying the Wear State of Grinding Wheels Based on VMD Denoising and AO-CNN-LSTM

Monitoring the condition of the grinding wheel in real-time during the grinding process is crucial as it directly impacts the precision and quality of the workpiece. Deep learning technology plays a vital role in analyzing the changes in sensor signals and identifying grinding wheel wear during the grinding process. Therefore, this paper innovatively proposes a grinding wheel wear recognition method based on Variational Mode Decomposition (VMD) denoising and Aquila Optimizer—Convolutional Neural Network—Long Short-Term Memory (AO-CNN-LSTM). The paper utilizes Acoustic Emission (AE) signals generated during grinding to identify the condition of the grinding wheel. To address noise interference, the study introduces the VMD algorithm for denoising the sample dataset, enhancing the effectiveness of neural network training. Subsequently, the dataset is fed into the designed Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM) structure with AO-optimized parameters. Experimental results demonstrate that this method achieves high accuracy and performance.

Multi-objective optimisation of a metro wheel profile to reduce wheel flange wear and RCF considering the distribution density of wheel−rail contact points

In this study, the problem of severe wheel flange wear and tread rolling contact fatigue (RCF) of metro wheels with two wheel profiles, i.e. the LM-30 profile and the DIN5573-30 profile, is investigated. The contact characteristics of the two profiles are compared to analyse the causes of wheel flange wear and RCF. Indices combining the density distribution of contact points are proposed to evaluate the wheel flange wear and RCF. The optimisation region of the wheel profile is defined by eight arc parameters, which is derived from the tangency of the arcs. A back-propagation neural network is constructed as a surrogate model for the design variables and the objective function. Four intelligent optimisation algorithms are compared and used to solve the optimisation problem. The vehicle—track dynamic model and the wheel wear and RCF prediction model are used to verify the performance of the optimised profile. The results show that the critical speed of the optimised profile is between that of the LM profile and that of the DIN5573 profile, and the curving performance of the optimised profile is significantly improved. At the same time, the optimised wheel profile can reduce wheel flange wear and tread RCF.

Analysis of Grindability and Surface Integrity in Creep-Feed Grinding of High-Strength Steels.

Creep-feed grinding of high-strength steel is prone to excessive wheel wear and thermal damage defects, which seriously affects the service performance of parts. To solve the above-mentioned issue, a creep-feed grinding test was carried out on high-strength steel using SG and CBN abrasive wheels. The grindability of high-strength steel was scrutinized in terms of grinding force, machining temperature and grinding specific energy. Moreover, the effects of operation parameters and grinder performances on the surface integrity of the workpiece such as surface morphology, roughness, residual stress and hardness were rigorously studied. The results indicate that, when the instantaneous high temperature in the grinding area reaches above the phase transition temperature of the steel, the local organization of the surface layer changes, leading to thermal damage defects in the components. The outstanding hardness and thermal conductivity of CBN abrasives are more productive in suppressing grinding burns than the high self-sharpening properties of SG grits and a more favorable machining response is achieved. The effects of thermal damage on the surface integrity of high-strength steel grinding are mainly in the form of oxidative discoloration, coating texture, hardness reduction and residual tensile stresses. Within the parameter range of this experiment, CBN grinding wheel reduces grinding specific energy by about 33% compared to SG grinding wheel and can control surface roughness below 0.8 µm. The weight of oxygen element in the burn-out workpiece accounts for 21%, and the thickness of the metamorphic layer is about 40 µm. The essential means of achieving burn-free grinding of high-strength steels is to reduce heat generation and enhance heat evacuation. The results obtained can provide technical guidance for high-quality processing of high-strength steel and precision manufacturing of high-end components.

The effectiveness of different wear indicators in quantifying wear on railway wheels of freight wagons

Railway infrastructure relies on the dynamic interaction between wheels and rails; thus, assessing wheel wear is a critical aspect of maintenance and safety. This paper focuses on the wheel–rail wear indicator T-gamma (Tγ). Amidst its use, it becomes apparent that Tγ, while valuable, fails to provide a comprehensive reflection of the actual material removal and actual contact format, which means that using only Tγ as a target for optimization of profiles is not ideal. In this work, three different freight wagons are evaluated: a meter-gauge and a broad-gauge heavy haul vehicles from South American railways, and a standard-gauge freight vehicle operated in Europe, with different axle loads and dissimilar new wheel/rail profiles. These vehicles are subjected to comprehensive multibody simulations on various tracks. The simulations aimed to elucidate the intricate relationship between different wear indicators: Tγ, wear index, material removal, and maximum wear depth, under diverse curves, non-compensated lateral accelerations (Anc), and speeds. Some findings showed a correlation of 0.96 between Tγ and wear depth and 0.82 between wear index and material removed for the outer wheel. From the results, the Tγ is better than the wear index to be used when analyzing wear depth while the wear index is more suited to foresee the material lost. The results also show the low influence of Anc on wear index and Tγ. By considering these factors together, the study aims to improve the understanding of wheel–rail wear by selecting the best wear analysis approaches based on the effectiveness of each parameter.

Review Kerusakan Keausan Roda Pada Kendaraan Kereta Api

Damage due to wheel wear on railway trains has a significant impact on railway safety and comfort. This review examines various aspects related to wheel wear damage on trains. The primary focus of this review encompasses three critical areas: railway track, wheel-rail interaction, and the trains themselves. The first section discusses the structure and modeling of railway tracks, while the second section explores various types of interactions between wheels and rails as well as related mathematical models. The third section reviews the types of railway vehicles, their mathematical models, and their stability on straight and curved tracks. Furthermore, this review also examines the influence of wheel wear on the dynamic response of the system. It is hoped that this review will provide valuable insights for practitioners and researchers in improving and enhancing the reliability and safety of railway systems.

A Review of Wheel Wear Damage in Railway Vehicle

A Review of Wheel Wear Damage in Railway Vehicle

Effect of line parameters on wheel wear of heavy–haul freight vehicle

Purpose The serious wear problem of heavy-haul freight vehicle wheels affects the safety and economy of vehicle operation. This paper aims to study wheel wear evolution law and the influence of line parameters on wheel wear of heavy-haul freight, and provide the basis for operation and line maintenance. Design/methodology/approach The wheel wear test data of heavy-haul freight vehicles were analyzed. Then a heavy-haul freight vehicle dynamic model was established. The line parameters influencing wheel wear in heavy-haul freight vehicles were also analyzed by the Jendel wear model, and the effects of rail cant, rail gauge, rail profile and line ramp on wheel wear were analyzed. Findings A rail cant of 1:40 results in less wheel wear; an increase in the rail gauge can reduce wheel wear; and when matched with the CHN60 rail, the wear depth is relatively small. A decrease of 9.21% in wheel wear depth when matched with the CHN60 rail profile. The ramp of the heavy-haul line is necessary to consider for calculating wheel wear. When the ramp is considered, the wear depth increases by 8.47%. The larger the ramp, the greater the braking force and therefore, the greater of the wheel wear. Originality/value This paper first summarizes the wear characteristics of wheels in heavy-haul freight vehicles and then systematically analyzes the effect of line parameters on wheel wear. In particular, this study researched the effects of rail cant, rail gauge, rail profile and line ramp on wheel wear. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2024-0038/

Wheel wear prediction for high-speed trains by considering wheel-rail elastic deformation

Wheel-rail geometric parameters are crucial for determining wheel wear in high-speed trains. Under the action of a wheel-rail load, both the wheel and rail suffer elastic deformation, which affects the wheel-rail contact relationship and further influences the wheel profile and its evolution. In this study, a field test was conducted on a high-speed train operating at 250 km/h, and the worn wheel profiles and wear curves were continuously measured for one reprofiling cycle. Subsequently, a vehicle dynamics model is built using a wheel wear prediction model based on the integrated USFD wear algorithm. In this model, the finite element model of the wheel-rail contact is considered. The wheel-rail geometric parameters are obtained by determining their elastic deformation through the finite element method, which considers the effect of three parameters: the track gauge, back-to-back space of the wheelset, and rail cant. After considering the wheel-rail elastic deformation, the track gauge decreases from 1435 to 1434.5 mm, the back-to-back space varies from 1353 to 1352.3 mm, and the rail cant changes from 1:40 to approximately 1:37. Finally, the simulation and experimental results are compared, revealing that the wheel-rail elastic deformation has a significant impact on the wheel wear after the vehicle travels 150,000 km. The wear depth and wheel-rail equivalent conicity after considering the elastic deformation are closer to the measured results.

A two-step data-driven method for predicting the wear of train wheel treads using GA-BPNN and LSTM algorithms

ABSTRACT During train operations, the changing of wheel tread profile due to wheel-rail contact has a significant impact on the safe operation of the train and passenger comfort. Therefore, it is crucial to timely monitor the condition of the wheels and accurately predict wheel wear. In addition, wheel wear prediction can also guide wheel re-profiling, thereby extending the service life of wheels and minimizing operating costs. In this paper, a two-step data-driven wear prediction method was proposed to comprehensively predict wheel wear from two perspectives: the overall train and individual wheels. Firstly, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Radial Basis Function neural network (RBFNN) were used to preprocess historical data, remove outliers, and construct time series data. By analysing the wear trend of wheels, a two-step data-driven prediction method was proposed. The first step is to predict the average Wear of wheels in the future time, and the second step is to predict the deviation between each wheel Wear value and the mean in the future time. Different prediction models were utilized and compared to obtain the best predictive performance. Results show that back propagation neural network (BPNN) optimized by genetic algorithm (GA) and long short-term memory (LSTM) based model yield better performance for predicting the wear trend of the overall train and individual wheels respectively. In addition, comprehensive contour parameters were utilized in the prediction model to enhance the accuracy and robustness.