Designing and assessing wheel/rail profiles for improved rolling contact fatigue and wear performance

Magel, Eric; Kalousek, J

doi:10.1177/0954409717708079

Cited by 17 publications

(11 citation statements)

References 28 publications

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“…By considering the simulation result of the bogie and the relevant track parameters for wheel-rail wear, using the optimization tools of design of experiment in ADAMS facilitated the proposal of improvement measures, which include adopting a double-acting roller elastic side bearing, increasing the lateral and longitudinal clearances of the side-frame box guide, and increasing curve super-elevation, etc. [15] [16] [17] [18] [19]. Experiment design may analyse the change of multi-design variable simultaneously.…”

Section: Improvement Scheme For Reducing Wheel-rail Wearmentioning

confidence: 99%

Research on Wheel Rail Wear for a 140 t Open Type Hot Metal Car

Feng¹,

Jia²,

Gao³

2020

ENG

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To maintain the safety of an open-type hot-metal car and to reduce wheel-rail wear during transportation, simulation models of the main components of such car were built using Pro/E software and then tested. In particular, the Pro/E models were imported into ADAMS/Rail for assembly and then used to construct a complete hot-metal car dynamic model. Locomotive wheel-rail attack angle, wheel-rail lateral force, and wear index were used as evaluation parameters during the simulation to analyze the effects of bogie parameter, rail parameter, and speed of the hot-metal car on wheel-rail wear. An improvement scheme for reducing wheel-rail wear was proposed based on the result of the dynamic simulation, wherein wheel-rail wear and curving performance were analyzed and compared. The simulation provided an important reference for evaluating and improving the dynamic performance of the hot-metal car. The applied effect showed that the improvement scheme is effective.

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Section: Improvement Scheme For Reducing Wheel-rail Wearmentioning

confidence: 99%

Research on Wheel Rail Wear for a 140 t Open Type Hot Metal Car

Feng¹,

Jia²,

Gao³

2020

ENG

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“…With the continuous increase in the loading capacity and operational velocity of railway systems, frequent high-stress contacts exist between the train wheels and the tracks, which aggravate the deterioration and produce railhead defects [ 1 , 2 ]. Although material [ 3 ] and construction [ 4 ] technologies evolved to improve rail durability, surface damages by continuous fatigue, wear, and harsh environments are unavoidable [ 5 , 6 , 7 ]. If not addressed, the initial damages will develop into severe transverse defects, including squats, head checking, and gauge corner collapse [ 8 ], which have resulted in numerous derailment incidents [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Scattering and Neural Networks for Railhead Defect Identification

Yang

2021

Materials

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Accurate and automatic railhead inspection is crucial for the operational safety of railway systems. Deep learning on visual images is effective in the automatic detection of railhead defects, but either intensive data requirements or ignoring defect sizes reduce its applicability. This paper developed a machine learning framework based on wavelet scattering networks (WSNs) and neural networks (NNs) for identifying railhead defects. WSNs are functionally equivalent to deep convolutional neural networks while containing no parameters, thus suitable for non-intensive datasets. NNs can restore location and size information. The publicly available rail surface discrete defects (RSDD) datasets were analyzed, including 67 Type-I railhead images acquired from express tracks and 128 Type-II images captured from ordinary/heavy haul tracks. The ultimate validation accuracy reached 99.80% and 99.44%, respectively. WSNs can extract implicit signal features, and the support vector machine classifier can improve the learning accuracy of NNs by over 6%. Three criteria, namely the precision, recall, and F-measure, were calculated for comparison with the literature. At the pixel level, the developed approach achieved three criteria of around 90%, outperforming former methods. At the defect level, the recall rates reached 100%, indicating all labeled defects were identified. The precision rates were around 75%, affected by the insignificant misidentified speckles (smaller than 20 pixels). Nonetheless, the developed learning framework was effective in identifying railhead defects.

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“…12 Magel and Kalousek designed and assessed wheel/rail profiles for improving the rolling contact fatigue and the wear performance. 13…”

Section: Introductionmentioning

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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Designing and assessing wheel/rail profiles for improved rolling contact fatigue and wear performance

Cited by 17 publications

References 28 publications

Research on Wheel Rail Wear for a 140 t Open Type Hot Metal Car

Research on Wheel Rail Wear for a 140 t Open Type Hot Metal Car

Wavelet Scattering and Neural Networks for Railhead Defect Identification

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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