Predicting railway wheel wear under uncertainty of wear coefficient, using universal kriging

Cremona, Marzia A.; Liu, Binbin; Bruni, Stefano; Lewis, Roger

doi:10.1016/j.ress.2016.05.012

Cited by 35 publications

(22 citation statements)

References 26 publications

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“…There, the dissipated energy is considered as an image of the heat and the material worn at the contact level during traction. This assumption is very similar to the Archard's equation that is more commonly used in railway industry for wear prediction (see [5] and [6]). Thus, a deterioration index is obtained as:…”

Section: Deterioration-based Fault Detectionmentioning

confidence: 85%

Condition monitoring of a friction drive system

Rodrı́guez

Martínez

Bérenguer

2017

2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC)

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This paper presents an approach for condition monitoring of a friction drive system, based on a recently developed deterioration estimation approach which includes the mechanical device dynamics and the dynamics of the deterioration at the joint. In this work a fault detection and a fault estimation application are proposed. Here, a fault is considered as an abnormal condition of the joint, mainly linked to the quality of the materials. For these purposes, we assume the knowledge of the input current signal and angular-speed sensor information. An architecture for condition monitoring is illustrated with abrupt fault and incipient fault simulated scenarios.

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Section: Deterioration-based Fault Detectionmentioning

confidence: 85%

Condition monitoring of a friction drive system

Rodrı́guez

Martínez

Bérenguer

2017

2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC)

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“…By simulating actual tracks, scholars have obtained experimental wear data, conducted research on the wear coefficient k in the Jendel model using a statistical method, and developed wear coefficient probability graphs applicable to specific track and wheel materials in recent years [31], [32]. These graphs are more detailed compared to the diagram showing the wear coefficient distribution determined by statistics using the Jendel model.…”

Section: Jendel Wear Modelmentioning

confidence: 99%

Research on Prediction of Metro Wheel Wear Based on Integrated Data-Model-Driven Approach

Zhu

Yang

et al. 2019

IEEE Access

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Existing research on wheel wear prediction uses either data-driven or model-based methods. However, due to the high reliability and limited sample characteristics of metro wheel wear, data-driven methods are not accurate enough and require relatively high data costs, and model-based methods mainly lack verification with measured data and generalization ability. To address the shortcomings of the two types of methods, a new approach combining model-based and data-driven methods is used to predict wheel wear in this paper. First, the least-squares algorithm is used to analyze and calculate the difference between the wear measurement for a specific running mileage and the corresponding simulated wear, with the minimum difference taken as an objective function. By means of optimization algorithms including Genetic Algorithm, Particle Swarm Optimization, Tabu Search and Simulated Annealing, the wear coefficient k in Jendel wear model is optimized, thereby obtaining an optimized Jendel wear model. Later, metro wheel wear for additional running mileage is simulated and predicted through combined application of the vehicle system dynamics, wheel-rail contact, and optimized Jendel wear models. Finally, the paper analyzes the wear prediction results obtained by the integrated data-model-driven approach and compares them with the results of traditional methods and measured data. The results suggest that the integrated data-model-driven approach effectively reduces the uncertainty in selecting the wear coefficient by experience, lowers the experimental data costs, and improves the wear prediction accuracy. Therefore, it is a promising approach to wheel wear prediction. INDEX TERMS Metro wheel, wear prediction, data-model driven, optimization algorithms, Jendel wear model.

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“…Cremona et al. 22 proposed a methodology based on the equation developed by Archard to forecast the volume of material loss and the remaining service life of a wheel. In addition, they constructed a continuous wear coefficient map according to the dependence of wear coefficient measurements with similar contact pressures and sliding speeds.…”

Section: Introductionmentioning

confidence: 99%

Numerical prediction of rail wear development in high-speed railway turnouts

Wang

2020

Proceedings of the Institution of Mechanical Engineers, Part F:

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Wear of rails in turnouts is a common problem during the operation of high-speed railways. It can seriously affect the running safety of trains and the service lives of wheels and turnout rails. In this study, a numerical prediction model for rail wear development in high-speed railway turnouts was established. According to the material wear theory developed by Archard, the wear depth distribution in the wheel–rail contact patch was calculated based on a vehicle–turnout coupling dynamics simulation and wheel–rail rolling contact analysis. For the dynamics model, various components of the vehicle and complex nonlinear interactions between the components were simulated in detail to guarantee consistency with reality. The combination relationship of the switch and stock rails and the irregular and variable cross-sections of the rails in the switch panel of the turnout were considered. Spatial interpolation was used to achieve three-dimensional transitions between adjacent irregular cross-sections to model the compromised rails in the turnout. In addition, the stiffness and damping characteristics of the track in the turnout zone were taken into account. The rail wear rates for every characteristic section of the switch panel were calculated by the superposition model for rail profile wear. An adaptive-step algorithm was adopted in the iterative computations to update the rail profiles for every characteristic section position, which could reduce the cumulative errors and effectively improve the stability and reliability of the numerical model. Finally, case studies were conducted to investigate the wear developments of the switch and stock rails of high-speed turnouts using the developed model. In addition, the rail wear status of turnouts in the Shanghai–Nanjing high-speed railway was measured. The numerical prediction results are consistent with those of the actual situations in the field, verifying the rationality of the established model. This work shows the potential for guiding the maintenance and optimal design of turnouts and improving the understanding of the formation mechanism and influencing factors of rail wear in turnouts.

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Predicting railway wheel wear under uncertainty of wear coefficient, using universal kriging

Cited by 35 publications

References 26 publications

Condition monitoring of a friction drive system

Condition monitoring of a friction drive system

Research on Prediction of Metro Wheel Wear Based on Integrated Data-Model-Driven Approach

Numerical prediction of rail wear development in high-speed railway turnouts

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