Optimizing the re-profiling strategy of metro wheels based on a data-driven wear model

Xu, Hong; Yuan, Hua; Zhao, Wenyu; Chen, Xiai

doi:10.1016/j.ejor.2014.10.033

Cited by 24 publications

(21 citation statements)

References 17 publications

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“…During the rail grinding process, the rail defects and surface unevenness can be eliminated by peripheral grinding method with the help of a grinding wheel [5,6]. During the wheel re-profiling process, the flaws existing on the wheel tread and wheel flange can be removed by a turning tool to keep the acceptable profile, which can help to extend the wheel service life [7,8]. Aiming at maintaining the appropriate profiles of the rail and wheel to ascertain the smooth and secure operation of trains, rail grinding and wheel re-profiling are very essential for the timely maintenance of railway infrastructure, which directly improves the wheel/rail contact conditions.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation on the Wear and Damage Characteristics of Machined Wheel/Rail Materials under Dry Rolling-Sliding Condition

Liu

Quan

Wan

et al. 2020

Metals

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To guarantee the smooth operation of trains, rail grinding and wheel turning are necessary practices to remove surface defects. Surface integrity of machined wheel/rail materials is significant to affect their tribological performance. In this paper, firstly, the wheel specimens were turned by a CNC lathe and the rail specimens were ground by a cylindrical grinding machine with various machining parameters. Then, the wear and damage behavior of the machined wheel/rail discs was systematically investigated via a twin-disc wear testing apparatus under dry rolling-sliding condition. The experimental results show that the surface hardness of rail discs after machining is slightly higher than that of wheel discs, while the surface roughness and plastic deformation layer of wheel discs are much larger than those of rail discs. The surface hardness increase degree of rail discs and their thickness of plastic deformation layer are greater than those of wheel discs after the rolling-sliding test. The wear loss of wheel discs is much larger than that of rail discs. Surface roughness, hardness and plastic deformation layer of wheel/rail discs after machining exert a comprehensive effect on the wear behavior, and friction pair with appropriate original surface hardness and roughness generates the smallest amount of wear loss.

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Section: Introductionmentioning

confidence: 99%

Experimental Investigation on the Wear and Damage Characteristics of Machined Wheel/Rail Materials under Dry Rolling-Sliding Condition

Liu

Quan

Wan

et al. 2020

Metals

View full text Add to dashboard Cite

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“…The wheel pair is called one of the three major consumable parts of the train, and the wear condition of the rim and the tread is a key factor affecting the safe and stable operation of the train, the ride comfort, and the service life of the wheel track. 1,2 As the train causes continuous wear of the wheel-set during the operation, the related expenses of maintenance and repair such as repairing or replacing it in time are one of the important components of the train maintenance cost. 3 Therefore, on the basis of mastering the historical state of the wheel-set, predicting the hidden danger index of the wheel-set is beneficial to the railway management department to formulate effective maintenance measures in time to improve the operational safety, reliability, and economy of the train.…”

Section: Introductionmentioning

confidence: 99%

Optimization of LMBP high-speed railway wheel size prediction algorithm based on improved adaptive differential evolution algorithm

Zhang

Luo

Gao

2019

International Journal of Distributed Sensor Networks

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It is beneficial for maintenance department to make maintenance strategy and reduce maintenance cost to forecast the hidden danger index value. Based on the analysis of the research status of wheel-to-life prediction at home and abroad and the repair of wheel-set wear and tear, this article designs and implements an adaptive differential evolution algorithm Levenberg–Marquardt back propagation wheel-set size prediction model. Aiming at the shortcomings of back propagation neural network, it is easy to fall into local extreme value. The back propagation algorithm is improved by Levenberg–Marquardt numerical optimization algorithm. Aiming at the shortcomings of back propagation neural network algorithm for randomly initializing connection weights and thresholds to fall into local extreme value, the differential evolution algorithm is used to optimize the initial connection weights and thresholds between the layers of the neural network. In order to speed up the search of the optimal initial weights and thresholds of the differential evolution algorithm Levenberg–Marquardt back propagation neural network, the initial values are further optimized, and an adaptive differential evolution algorithm Levenberg–Marquardt back propagation wheel-set size prediction model is designed and implemented. Compared with the proposed combine adaptive differential evolution algorithm with LMBP optimization (ADE-LMBP) is effective and significantly improves the prediction accuracy.

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“…Some research has been conducted on service life prediction for railway vehicle wheels using a datadriven method. In one study [16], [17], a Monte Carlo statistical method was adopted to predict the service life of wheels from significant amounts of measured wheel wear data from the Guangzhou Metro. In 2015, based on the data obtained from a knowledge base and the fuzzy logic system, Dordević et al [18] analyzed the influence of various factors on rolling stock failure, and evaluated the reliability of wheels.…”

Section: Introductionmentioning

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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Optimizing the re-profiling strategy of metro wheels based on a data-driven wear model

Cited by 24 publications

References 17 publications

Experimental Investigation on the Wear and Damage Characteristics of Machined Wheel/Rail Materials under Dry Rolling-Sliding Condition

Experimental Investigation on the Wear and Damage Characteristics of Machined Wheel/Rail Materials under Dry Rolling-Sliding Condition

Optimization of LMBP high-speed railway wheel size prediction algorithm based on improved adaptive differential evolution algorithm

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

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