Wheel Profile Measurements Research Articles

Probabilistic review of wheel profiles based on hollow tread in the U.S. heavy haul rail network

Wheel profile is one of the most critical factors that governs the dynamic interaction between railcar wheels and the rail. This paper quantifies and analyzes 15,000 wheel profiles that were randomly selected from a dataset obtained from wayside wheel profile measurement systems from railcars that are in unrestricted interchange in the North American Class I railroad network. Mean dimensions for each of the four critical wheel tread parameters were as follows; 30.5 mm (1.202 in.) for flange height, 35.4 mm (1.395 in.) for flange thickness, 0.483 mm (0.019 in.) for hollow tread, and 37.8 mm (1.488 in.) for rim thickness. Further evaluation focused on the magnitude of hollow tread, one of the important factors that determines wheel rail contact location, contact patch size, and dynamic interaction of the railcar and track. Correlative analysis revealed that hollow tread is linearly related to flange height, flange thickness, and rim thickness. Given this relationship, we categorized wheel profile data into characteristic bins of wheel profiles, and a new classification system based on hollow tread was proposed that should be considered when designing track infrastructure components (e.g., turnout frogs). Combinations of parameters were derived and organized into five wheel-profile classification ranges, based on “most likely” to “least likely” profiles that might be encountered. For the five classifications (Type A to Type E), a range of hollow tread from 0 mm to 5 mm (interval of 1 mm) and corresponding mean values for the three other parameters were applied. This probabilistic analysis and classification of wheel profiles into categories enables better understanding of current North American wheel conditions. Additionally, these data and the proposed analysis method can be leveraged to further optimize wheel profiles and track components, identify relationships with rates of rail surface defects and track degradation, and prioritize maintenance.

Measurement and evaluation of wheel profiles of a rail vehicle on an underfloor wheelset lathe

Wheel profile form directly affects the dynamic behaviour of rail vehicles. Moreover, it is an important factor to ensure the safe running of a high-speed vehicle. As off-line measurements do not provide high-efficiency and high-accuracy wheel profile measurements, we focused on on-machine measurement and evaluation for obtaining the wheel profiles of a rail vehicle in this study. By combining the wheel machining with the wheel measurement, the measurement and evaluation of radial and axial wheel profiles on the underfloor wheelset lathe were proposed. The measurement of radial wheel profile based on two laser sensors was introduced, and the corresponding error models of on-machine measurement were established. The error models were used to separate the measurement errors from the measurement data. This can improve the accuracy of measuring the radial wheel profile effectively. For measuring the axial wheel profile, an adaptive correction method of profile error was proposed to suppress the measurement error, which was the basis for obtaining an accurate equivalent conicity. By measuring radial and axial wheel profiles, polygonisation evaluation and equivalent conicity were obtained to evaluate the radial and axial dynamic behaviours of a wheelset, respectively. Finally, the measurements were applied to an underfloor wheelset lathe to verify the effectiveness of measuring wheel profiles. In this study, we not only improved working efficiency for the machine tools, but also provided a high-accuracy measurement method for the wheel profiles of rail vehicle.

Experimental validation of a fast wheel wear prediction model

Wheel-rail wear prediction is a fundamental matter in order to study problems such as wheel lifespan prediction and evolution of vehicle dynamic characteristics with time. However, one of the principal drawbacks of the existing methodologies for calculating the wear evolution is the computational cost. The developed methodology is based on (1) representing the whole route in a reduced number of characteristic points and (2) substituting dynamic simulations by quasi-static ones, in order to reduce computational cost significantly. This paper presents its integration into the complete simulation process for predicting wheel wear evolution and its validation by comparing simulated results with in-service wheel profile measurements. The generality of the method has been verified by applying it to two different operational cases with very different track characteristics. For the validation, the evolution of the worn depth in the tread has been compared according to the travelled distance, as well as the morphology of the worn depth (worn area). Promising results have been obtained in the prediction of wheel wear evolution: in both cases, the simulated curve approaches the measurement curve satisfactorily. Therefore, it can be concluded the validation of the developed method adequately.

An applied method for railway wheel profile measurements due to wear using image processing techniques

Accurate wear prediction on railway wheels and evolution of railway wheels profile can affect the maintenance planning. The objective of this paper is to provide a new applied method for measuring the railway wheel profile with photographing from the railway wheel to measure by image processing techniques. The aim of this new applied method is to measure the wheel profile using images taken from railway wheels and compare it with the original plan. For this purpose, images were taken by using a camera. In this study, all automatic correction options were turned off and brightness and contrast were in normal conditions. The pixel data is converted to double-type data and are placed in the range of zero and one. Then, the input image which is usually as a three-channel or RGB image is converted to a single channel or gray surface image. Images taken from wheel profiles are processed using image processing techniques. Then the lines, curves and shapes in the image are extracted as cross-sectional and continuous curves. The new applied method results by image processing method obtained show good agreement with those achieved in field measurements.

Research on measurement method of grinding wheel profile based on image mosaic

Grinding is one of the most effective processing methods of hard and brittle materials. In the grinding process, measurement of the grinding wheel profile is of great significance for evaluating the grinding wheel processing accuracy, detecting wear and compensating machining errors. In this paper, a novel grinding wheel profile measurement method based on an image mosaic is proposed to realize fast non-contact profile measurement. A profile template matching approach based on the profile search algorithm is constructed according to the characteristics of the wheel profile, and the complete grinding wheel profile is obtained and measured by a fast and accurate mosaic of profile images. The experimental results show that the proposed algorithm is more efficient and accurate compared with the traditional image mosaic algorithm. On the basis of this method, a measurement system for the grinding wheel profile is established and cylindrical and hemispherical grinding wheels are measured. The high-resolution complete cross-sectional profiles are obtained and the results are compared with a 2D laser profiler. The root mean squared error of the cylindrical grinding wheel is 0.08%–0.1% of the nominal radius. The center position deviation of the hemispherical grinding wheel is between ±3 µm and the radius deviation is below 0.08% of the nominal radius. The measurement results indicate the feasibility and precision of this method, which can realize non-contact rapid measurement of the grinding wheel profile.

Evaluation of wheel profile measurements by means of the contact-point function for the wheel–rail interface

This study examines the accuracy of a wayside train wheel profile measurement system. This was accomplished by an evaluation of the contact-point function for the wheel–rail interface. The wheel profile measurement system in question generates data about the wheel profiles of passing trains. These data are used for improving the wheel maintenance procedures for the rolling stock operator. Recent work shows that there are differences between the data from the two different units in the system, but how this influences further use of the data, e.g. in wheel–rail contact analysis, has not been investigated so far. Accordingly, this article shows how two key wheel measures (the wheel flange thickness and the wheel profile) impact on the contact-point function and which of these measures has the largest impact on the contact-point function. The data used in this study were generated by two different measurement units for the same wheel and with the same wheel status. The results show that the different units produce different results and that these differences are more prominent when a difference in the flange thickness is detected, with a resulting shift of the front side of the flange and of the tread. With no difference in the flange thickness, i.e. no shift of the front side of the flange and of the tread, a difference was still detected in the contact conditions. Furthermore, this investigation shows that the shape of the tread has a greater impact on the contact-point conditions compared to a change in the flange thickness of up to 2.5 mm. This difference in the tread shape could have originated in measurement noise or different wheel measurement positions. The results of the study also show the importance of managing the measurement quality before using the data, for example for maintenance decisions.

Prediction of wheel profile wear and crack growth – comparisons with measurements

A model which can predict the length of the surface crack and crack depth in rails was developed in a previous study by the authors B. Dirks, R. Enblom, A. Ekberg, M. Berg (2015) [1]. In the present study, verification of this crack prediction model in combination with a wear prediction model was done against wheel measurements. For a period of 15 months, the wheels of three units of a Stockholm commuter train were measured with respect to wear and crack development for verification of the wheel life prediction tool. Vehicle–track dynamics simulations were used to calculate the forces and contact positions for the wear and crack prediction models. It can be concluded that the wear prediction model gives reasonable results, especially considering the large scatter in the wheel profile measurements. Although the wheel life prediction tool could not be verified, since the crack prediction model had to be recalibrated for the current wheel application, the results appear promising.

Evaluating the measurement capability of a wheel profile measurement system by using GR&R

Reliable data with less variation play a key role for acceptance of the usefulness of the measurement output of a wheel profile measurement system (WPMS) in a railway network. However, in practice, ...

Condition monitoring and e-maintenance solution of railway wheels

Purpose – The purpose of this paper is to investigate the failure-driven capacity consumption of wheels on the track, to determine whether there are some relations to vehicle wheel configurations that show a larger amount of failures, and to ascertain the influence of the temperature and the travelling direction of the train on the number of events. This information can be used to develop prognostic health management so that more track capacity can be gained without modifications, re-building or re-investments. Design/methodology/approach – This paper presents a study of 1,509 warning and alarm events concerning train wheels. The data come from the infrastructure manager's wheel defect detectors and wheel profile measurement system. These data have been analysed and processed to find patterns and connections to different vehicles, travelling directions and temperatures. Findings – Lower temperatures increase the probability of wheels having high vertical forces. Trains with different wheel configurations ...

A study of railway wheel profile parameters used as indicators of an increased risk of wheel defects

The capacity demands on railways will increase in the future, as will demands for a robust and available system. The availability of a railway system is dependent on the condition of its infrastructure and rolling stock. To inspect rolling stock so as to prevent damage to the track due to faulty wheels, infrastructure managers normally install wayside monitoring systems along the track. Such systems indicate, for example, wheels that fall outside the defined safety limits and have to be removed from service to prevent further damage to the track. Due to the nature of many wayside monitoring systems, which only monitor vehicles at defined points along the track, damage may be induced on the track prior to fault detection at the location of the system. Such damage can entail capacity-limiting speed reductions and manual track inspections before the track can be reopened for traffic. The number of wheel defects must therefore be kept to a minimum. In this paper, wheel profile parameters measured by a wayside wheel profile measurement system, installed along the Swedish Iron Ore Line, are examined and related to warning and alarm indications from a wheel defect detector installed on the same line. The study shows that an increased wheel wear, detectable by changes in the wheel profile parameters, could be used to reduce the risk of capacity-limiting wheel defect failure events and their reactive measures.

Numerical assessment of the influence of worn wheel tread geometry on rail and wheel deterioration

Current maintenance criteria of railway wheel geometries consider mainly flange thickness, flange height, flange gradient, and depth of tread wear. These geometrical quantities are relatively simple to evaluate, but do not fully describe neither the worn profile nor its influence on subsequent deterioration of wheel and rail. With increased use of automatic wheel profile measurements there is a potential to plan maintenance from measures that provide a more elaborate description of the wheel profile and its operational capabilities. To this end, the paper first outlines a procedure to parametrise worn wheel profiles. This procedure is linked to multibody simulations of a curve negotiating freight wagon to identify parameters of the worn wheel profile with the largest influence on rolling contact fatigue (RCF) and wear. To limit the number of parameter combinations, nearly orthogonal and space filling Latin hypercube sampling is employed. Regression analysis is then utilised to derive simple relationships (meta-models) between geometry parameters and deterioration measures. The derived meta-models of RCF and wear deterioration are shown to produce good agreement to results from fully fledged multibody simulations. The end result is identified geometrical quantities that provide an improved categorisation of the deteriorated wheel tread geometry for improved maintenance decisions.

Condition monitoring at the wheel/rail interface for decision-making support

Many railway assets, such as wheels, suffer from increasing deterioration during operation. Good condition monitoring based on good decision-making techniques can lead to accurate assessment of the current health of the wheels. This, in turn, will improve safety, facilitate maintenance planning and scheduling, and reduce maintenance costs and down-time. In this paper, wheel/rail forces are selected as a parameter (feature) for the condition monitoring of wheel health. Once wheels are properly thresholded, determining their condition can help operators to define maintenance limits for their rolling stock. In addition, if rail forces are used as condition indicators of wheel wear, it is possible to use measurement stations that cost less than ordinary profile stations. These stations are located on ordinary tracks and can provide the condition of wheelsets without causing shutdowns or slowdowns of the railway system and without interfering with railway traffic. The paper uses the iron-ore transport line in northern Sweden as a test scenario to validate the use of wheel/rail forces as indicators of wagon and wheel health. The iron-ore transport line has several monitoring systems, but in this paper only two of these systems will be used. Wheel/rail force measurements are performed on curves to see how the vehicle negotiates the curve, and wheel profile measurements are done on tangent track not far away. The vehicles investigated are iron-ore wagons with an axle load of 30 tonnes and a loaded top speed of 60 km/h. The measurements are non-intrusive, since trains are moving and assets are not damaged during the testing process.

Wheel Wear Predictions and Analyses of High-Speed Trains

Wear of wheel profiles is one of the most critical issues affecting cost and performance of railway transportation systems. By altering the profile from the shape originally chosen by the designer, wear has a very large effect on vehicle dynamics, so that excessive modifications of the original have to be recovered by griding and re-profiling the wheel to its original profile. Clearly, with high-speed train development, wheel profile design has been driven by keeping high level of passenger comfort and running safety and low track aggressiveness performances. A reliable wheel profile wear prediction model is developed and applied to a vehicle operating high-speed rail lines in China. The methodology is based on wheel wear simulations which are performed based on actual track data, and pertinent operating conditions. The vehicle model is built in the Simpack MBS software utilising validated suspension models. The contact between wheel and rail is modelled with an equivalent ellipse method and Kalker’s simplified theory (FASTSIM). The wear modelling is based on Archard’s wear model. Finally, validation against wheel profile measurements is performed by simulating approximately 240,000 km of running. Comparisons between simulated and measured wheel profiles, including four wheel wear indexes are presented. The agreement is generally very good.

Effects of wheel/rail contact patterns and vehicle parameters on lateral stability

In North American freight service, lateral instability (vehicle hunting) is normally associated with light vehicles and high-speed operations. However, recent research conducted by Transportation Technology Center, Inc. (TTCI), under the Association of American Railroads’ Strategic Research Initiatives Program, has shown that some new designs of heavy axle load freight cars are now susceptible to hunting when fully loaded at normal freight train operating speeds. Undesirable wheel/rail (W/R) contact conditions interacting with the car body dimensions and inertial parameters, combined with the characteristics of the suspensions, contribute to hunting. A summary of the investigation is presented here, which includes results of the vehicle tests, wheel and rail profile measurements and analyses, and NUCARS®1 computer simulations. The effects of the vehicle suspension parameters and car body inertial parameters, combined with undesirable W/R contact patterns, are shown to have a significant influence on the vehicle's lateral stability performance, with potential consequences for damage to both vehicles and track, and increased potential for derailment.

On-Machine Profile Measurement of Arc Grinding Wheel in 3-Axis Machine Using Non-Contact Displacement Sensor

Grinding is a processing method that involves duplicating shape accuracy, so the shape accuracy of the grinding wheel plays a crucial role in machining accuracy. However, this accuracy is difficult to obtain. This paper describes an on-machine wheel profile measuring method that uses a non-contact displacement sensor to obtain the shape accuracy of an arc grinding wheel in a 3-axis aspheric surface grinding machine. This method involves data processing with object radius confined filtering, as well as evaluation using the methods of fitting residual error, measurement uncertainty, and machining result simulation. To verify the feasibility of this measuring method, experiments were performed using two diamond grinding wheels (FEPA D91 with grit size 75-90 μm, and FEPA D15 with grit size 10-25 μm). The experimental results indicate that the method is accurate enough to give the arc grinding wheel profile measurement, while the measuring uncertainty is of the same order of magnitude as the grit size (that is, tens of μm). Moreover, the simulation of the grinding effect with wheel profile measurement data can derive the relationship between the wheel profile error and the machining form error, and can be used to instruct the truing time determination for precision grinding.

Prediction of wheel profile wear—comparisons with field measurements

A wheel profile wear prediction tool is developed and applied to a vehicle operating the commuter rail network in Stockholm. The methodology is based on a load collective concept where time-domain simulations are performed based on actual track data, measured rail profiles as well as pertinent operating conditions. The vehicle model is built in the GENSYS MBS software utilising validated suspension models. The contact between wheel and rail is modelled with Hertzian theory and Kalker’s simplified theory (FASTSIM). The wear modelling is based on Archard’s wear model and the implementation, including laboratory measurements, is performed in cooperation with tribology experts at KTH Machine Elements. Finally, validation against consecutive wheel profile measurements is performed by simulating approximately 200,000 km of running. Comparisons between simulated and measured wheel profiles, including four scalar wear measures (flange thickness, flange height, flange inclination and area worn off), are presented. The agreement is generally very good.

The rusting of steel surfaces in contact

A model which can predict the length of the surface crack and crack depth in rails was developed in a previous study by the authors B. Dirks, R. Enblom, A. Ekberg, M. Berg (2015) [1]. In the present study, verification of this crack prediction model in combination with a wear prediction model was done against wheel measurements. For a period of 15 months, the wheels of three units of a Stockholm commuter train were measured with respect to wear and crack development for verification of the wheel life prediction tool. Vehicle–track dynamics simulations were used to calculate the forces and contact positions for the wear and crack prediction models. It can be concluded that the wear prediction model gives reasonable results, especially considering the large scatter in the wheel profile measurements. Although the wheel life prediction tool could not be verified, since the crack prediction model had to be recalibrated for the current wheel application, the results appear promising.