On integrated wheel and track damage prediction using vehicle–track dynamic simulations

Casanueva, Carlos; Enblom, Roger; Stichel, Sebastian; Berg, Mats

doi:10.1177/0954409717700988

Cited by 10 publications

(8 citation statements)

References 36 publications

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“…The wear of the wheel profile is mainly reflected in the tread and wheel flange. 13,14 The wheelset wear can be different with the increasing of running time. In order to maintain the running safety and dynamics performance of the running gear, the corresponding repair strategy of the wheelset must be studied.…”

Section: Statistical Analysis Of Wheelset Wearmentioning

confidence: 99%

Optimization of metro wheel reprofiling based on cluster analysis

Lin

Yong

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part F:

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According to the characteristics of the metro vehicles running on the same track leading to a similar wheel wear, a clustering method based on k-means is proposed. The weighted wheel flange height of Sh and qR, and wheel flange thickness of Sd are used as clustering parameters. This paper classifies the treads of the wheels to be repaired and obtains a typical worn wheel profile to reprofile the wheels. Under the premise of ensuring the operation safety of vehicles, the wheel profile optimization model is established for a typical worn wheel profile based on the minimum repair volume. The optimized profile is used as the reprofiling template for all wheels in the same classification. Finally, based on the reprofiling standard for the wheel diameter difference between the left and right wheels, the optimization reprofiling of the wheel profile is achieved. The research results show that the proposed optimization strategy base on cluster analysis can significantly reduce the material removal.

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Section: Statistical Analysis Of Wheelset Wearmentioning

confidence: 99%

Optimization of metro wheel reprofiling based on cluster analysis

Lin

Yong

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part F:

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“…However, comparing the cepstra of models 5 and 6 suggests that decreasing the amplitude of the rail roughness has a similar effect on the peak amplitude than increasing the amplitude of the wheel roughness. This can be explained by applying Equations (2)- (6) to Equation 7: The second term in Equation (11) shows that the power spectrum of the wheel profile is scaled by the power spectrum of the rail profile before the inverse Fourier transform is taken. Thus, the amplitude of the peak in the cepstrum at the wheel circumference can be interpreted as the relation between the contributions of the wheel and track to the relative displacement and hence to the dynamic wheel-rail interaction.…”

Section: Cepstrum Analysis Of Synthetic Datamentioning

confidence: 99%

“…Bogacz and Frischmuth [4] studied the rolling motion of a polygonalized railway wheel and in [5], a method for the computation of the wheel-rail surface degradation in a curve is explained. Casanueva et al [6] address the issues of model complexity, accuracy and the input needed for wheel and track damage prediction using vehicle-track dynamic simulations.…”

Section: Introductionmentioning

confidence: 99%

Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations

et al. 2021

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Continuous wheel condition monitoring is indispensable for the early detection of wheel defects. In this paper, we provide an approach based on cepstral analysis of axle-box accelerations (ABA). It is applied to the data in the spatial domain, which is why we introduce a new data representation called navewumber domain. In this domain, the wheel circumference and hence the wear of the wheel can be monitored. Furthermore, the amplitudes of peaks in the navewumber domain indicate the severity of possible wheel defects. We demonstrate our approach on simple synthetic data and real data gathered with an on-board multi-sensor system. The speed information obtained from fusing global navigation satellite system (GNSS) and inertial measurement unit (IMU) data is used to transform the data from time to space. The data acquisition was performed with a measurement train under normal operating conditions in the mainline railway network of Austria. We can show that our approach provides robust features that can be used for on-board wheel condition monitoring. Therefore, it enables further advances in the field of condition based and predictive maintenance of railway wheels.

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“…This paper applies the widely used Archard wear model for analysis. 20–23 The wear is proportional to the energy expended in the wheel–rail contact. This process can be described by the following equation where V w is the wear volume, k is the wear coefficient, N is the normal force, s is the sliding distance, and H is the hardness of the material.…”

Section: Comparison Of Wheel–rail Wear Developmentmentioning

confidence: 99%

Optimal profile design for rail grinding based on wheel–rail contact, stability, and wear development in high-speed electric multiple units

Feng

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part F:

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High-speed electric multiple units have numerous advantages. However, a number of critical maintenance issues arise in the operation of high-speed electric multiple units. The previous researches about rail profile design usually take only a single type of wheel profile into account, which would cause some other problems such as severe increase of hollow wear on the wheels. This study systematically investigates the influence of rail grinding on running stability and wear development in high-speed electric multiple units and designs a new rail profile as reference for grinding that takes all types of vehicle wheels running on a specific line into account, in order to design a ground rail which could match the wheel profile and thus improve the running stability of electric multiple units. All types of wheel profiles used on the Wuhan–Guangzhou railway line are taken as the design reference. A wheel–rail wear simulation program is constructed based on CONTACT numerical simulation software and SIMPACK vehicle system dynamics software. The simulation results show that both the wheel–rail contact relationship and the running stability of high-speed electric multiple units improved after rail grinding. The results of the wheel wear analysis show that when the rail is ground to the target profile, after a running mileage of 200,000 km, the wear area of the new wheel profile LMA and the greatest hollow wear wheel profile LMA-25 decreases by 1.13 mm2 and 9.86 mm2, respectively. In addition, this method can prolong the wheel reprofiling interval. For the Wuhan–Guangzhou railway line, normally the grinding interval for the tangent track and large-radius curve is 2–3 years, and for the entering and exiting tunnel sections, the grinding interval should be set for 1–2 years, which could remove the damaged layer of the rail surface and could restore the designed profile of the rail and prolong the rail service life.

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On integrated wheel and track damage prediction using vehicle–track dynamic simulations

Cited by 10 publications

References 36 publications

Optimization of metro wheel reprofiling based on cluster analysis

Optimization of metro wheel reprofiling based on cluster analysis

Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations

Optimal profile design for rail grinding based on wheel–rail contact, stability, and wear development in high-speed electric multiple units

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