Prediction of the coexistence of rail head check initiation and wear growth

Zhou, Yu; Han, Yanbin; Mu, Dongsheng; Zhang, Congcong; Huang, Xuwei

doi:10.1016/j.ijfatigue.2018.03.027

Cited by 20 publications

(12 citation statements)

References 34 publications

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“…In the critical plane theory, the plane of fatigue initiation is the plane with the maximum fatigue damage parameter FP. Taking the J-S model as an example, FP is calculated below 9,24,31 :…”

Section: Critical Plane Model For Rail Hc Initiationmentioning

confidence: 99%

“…When the measured HC length of the upper specimen reached 0.1 mm, the number of roll cycles was considered as the initiation life of HC. 9 In order to eliminate measurement errors, 10 twin-roller fatigue tests (number of cycles: 0.5-5 Â 10 5 ) were conducted under the same working conditions and the number of cracks was counted (Table 3). Cracks were counted in the same area for each group of specimen.…”

Section: Validationmentioning

confidence: 99%

“…[6][7][8] Moreover, due to the wide application of rails with good wear resistance, such as heat-treated rails and alloy rails, the wheel-rail interaction is complex and harsh and requires to be further explored. 9 Madge et al [10][11][12] combined Archard model with fretting fatigue analysis to investigate the influences of material removal on the fretting fatigue life of Ti-6Al-4 V. Then, with different models, the interaction between fatigue and fretting wear was extensively explored. [13][14][15][16] With the critical plane model developed by Fatemi and Socie, 17 Zhu et al 18,19 and Liao et al 20 analyzed the fatigue reliability and predicted the fatigue life of turbine discs.…”

Section: Introductionmentioning

confidence: 99%

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Prediction model of rail head check initiation considering wear and nonlinear fatigue damage accumulation

Li,

Weng,

Zhou

et al. 2023

Fatigue Fract Eng Mat Struct

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Wear and rolling contact fatigue (RCF) cracks are the key defects affecting the service life of heavy‐haul railways in China. In this study, based on the influences of the loading sequence and the interaction between adjacent loads, a modified nonlinear fatigue damage accumulation model under the steady‐state wheel–rail contact was developed to predict the coexistence of wear growth and head check (HC) initiation. A twin‐roller fatigue test was designed and performed, and the testing data were compared with the predicted data to verify the established model. Compared with the linear model, the nonlinear model yielded the faster fatigue damage with an HC initiation life of 2.58 × 105 wheel cycles, which was 24.34% less than that of the linear model. In addition, the HC initiation lives obtained from the nonlinear and linear models were close to the median and upper limits observed in the field, respectively. The results of the linear and nonlinear models were respectively 41.3% and 22.5% higher than the two‐round test results. The result of the nonlinear model was closer to the two‐round test result of 2 × 105.

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Section: Critical Plane Model For Rail Hc Initiationmentioning

confidence: 99%

Section: Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction model of rail head check initiation considering wear and nonlinear fatigue damage accumulation

Li,

Weng,

Zhou

et al. 2023

Fatigue Fract Eng Mat Struct

Self Cite

View full text Add to dashboard Cite

show abstract

“…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 ]. Therefore, a non-destructive technology to discover the surface defects is significant for the security of railway systems.…”

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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“…The development of cracks has attracted the interests of many researchers. Fatigue crack initiation and growth in rail [4][5][6][7], fracture instabilities at crack tips of hyperelastic materials like rubber [8][9], as well as fracture mechanics analysis of cracks in plain and reinforced concrete [10][11][12] are all fascinating research topics. The rubber-to-metal bond failure was also studied [13].…”

Section: Introductionmentioning

confidence: 99%

Experimental and numerical investigation of damage development in embedded rail system under longitudinal force

Wang

Chen

et al. 2020

Engineering Failure Analysis

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Embedded rail system (ERS) is a new type of track structure with many advantages due to its continuous rail support. The rapid development of urban rail transit all over the world renders its application prospect broad. However, the cracks and debonds in ERS present a threat to the traffic safety and a possibility for high maintenance costs. In this work, a longitudinal pushing experiment was designed to explore the damage development process in ERS in order to help structural optimization and performance maintenance. The first order derivative of displacementlongitudinal force curve indicates that the damage process of ERS could be divided into three stages: linear elasticity, damage initiation and damage acceleration stages. The surface deformation of the elastic poured compound (EPC) was analyzed with the particle velocimetry and it is shown that the damage is possibly localized in a small EPC part. Statistics of the absolute displacements of a large number of interrogation areas show that their percentage distribution changes in agreement with the increment of rail displacement, which could be the basis for monitoring of EPC deformation in the breathing zone of continuous welded rail. The analysis of the deformation of EPC from side views, together with the qualitative analysis with finite element method, reveals that the large shear strain of rubber strip and the intense shear strain of EPC at rail foot are the main causes of damage initiation and growth in ERS under longitudinal force.

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Prediction of the coexistence of rail head check initiation and wear growth

Cited by 20 publications

References 34 publications

Prediction model of rail head check initiation considering wear and nonlinear fatigue damage accumulation

Prediction model of rail head check initiation considering wear and nonlinear fatigue damage accumulation

Wavelet Scattering and Neural Networks for Railhead Defect Identification

Experimental and numerical investigation of damage development in embedded rail system under longitudinal force

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