Prediction Method of Heavy Load Wheel/Rail Wear Mechanical Properties Based on GA-BP Hybrid Algorithm
DOI:
https://doi.org/10.13052/ejcm2642-2085.3134Keywords:
GA-BP hybrid algorithm, heavy duty wheel/rail, wear, mechanical property prediction, wheel/rail contact model, mechanical model of rail wearAbstract
Due to its large axle load and high-density operation mode, heavy haul transportation has greatly improved the cargo transportation capacity, and is receiving unprecedented attention from all countries in the world. Since the development of heavy haul freight transport in China, wheel rail wear has been paid much attention, especially the use of heavy axle load locomotives on upgraded heavy haul lines, which makes reducing wheel rail wear and damage become a technical problem to be solved urgently. Considering that there are too many mechanical parameters involved in the prediction of heavy load wheel rail wear mechanical properties, the prediction accuracy is reduced. Therefore, this paper proposes a method based on GA-BP hybrid algorithm to predict the mechanical properties of heavy load wheel/rail wear. Hertz contact theory is used to simplify the wheel rail contact relationship, and the wheel rail contact model is established. According to the wheel/rail contact model, the expressions of heavy load wheel/rail in the case of vertical, horizontal, direction and gauge irregularity are analyzed, and based on this, a mechanical model of heavy load wheel/rail wear is established. In order to solve the problems of slow convergence speed and easy to fall into local optimum of BP neural network in the prediction of heavy load wheel/rail wear mechanical properties, the global convergence of genetic algorithm is used to optimize the BP network. According to the obtained mechanical parameters of heavy load wheel/rail wear, the mechanical parameters are input into the optimized model, and the relevant prediction results are output. So far, the research on the prediction method of heavy load wheel/rail wear mechanical properties based on GA-BP hybrid algorithm has been realized. The experiment is designed from three aspects of wear degree, hardness and tensile strength, and compared with the measured value, reference [4] method, reference [5] method and reference [6] method to verify the effectiveness of the proposed method. The experimental results show that the predicted results of wear degree, hardness and tensile strength by this method are closer to the measured results. It is proved that the proposed method has higher prediction accuracy and better practical application effect.
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References
Peng Q, Ren S, Wang W, et al. The Railway Detection via Adaptive Multi-scale Fusion Processing[J]. Journal of Physics: Conference Series, 2021, 1887(1):012003–012012.
Pires A C, Mendes G R, Santos G, et al. Indirect identification of wheel rail contact forces of an instrumented heavy haul railway vehicle using machine learning[J]. Mechanical Systems and Signal Processing, 2021, 160(1):107806–107817.
Yan S, Deng Y, Hao L, et al. Analysis of Subgrade Vibration and Dynamic Stress of Heavy Haul Railway with High Embankment based on ZL-TNTLM Program[J]. Journal of Physics: Conference Series, 2021, 1965(1):012038–012049.
Cui J T, Guo Y. Research on Wheel/Rail Wear Prediction Model Based on Multivariable Parallel Discrete Computing Method[J]. Mechanical Engineer, 2022(02):130–134.
Wang M, Wang H L, Wang J P. Wear Volume Prediction of Brake Shoes Used in Heavy Freight Wagon Based on DBN[J]. Computer Simulation, 2022, 39(03):134–139.
Tao H Q, Cai X, Zhou Y. Prediction of Metro Wheel Tread Wear Based on Variable Weight Combination Model[J]. Urban Mass Transit, 2020, 23(06):58–62.
Xu W W, Peng J P, Qiu C R. Prediction model of subway pantograph slide pan wear trend based on LSSVR[J]. Railway Computer Application, 2020, 29(01):77–81.
Dong Y G, Yi S, Huang X L, et al. Temperature Distribution of Treads and Wear Prediction during Train Emergency Braking[J]. China Mechanical Engineering, 2021, 32(04):431–438+
Andrzej Myslinski, Andrzej Chudzikiewicz. Wear modelling in wheel-rail contact problems based on energy dissipation[J]. Tribology – Materials, Surfaces & Interfaces, 2021, 15(2):138–149.
Sladkova L. A., Neklyudov A. N. A New Look at the Problem of Wear on Wheel Rolling[J]. Journal of Friction and Wear, 2021, 42(2): 101–105.
Mayba I. A., Glazunov D. V. Optimization of Tribotechnical Characteristics of Wheel–Rail Friction Modifiers[J]. Journal of Friction and Wear, 2021, 41(6):517–520.
Zhang H, Ran X, Wang X, et al. Coupling Effects of Yaw Damper and Wheel-Rail Contact on Ride Quality of Railway Vehicle[J]. Shock and Vibration, 2021,15(3):1–18.
Pires A C, Mendes G R, Santos G, et al. Indirect identification of wheel rail contact forces of an instrumented heavy haul railway vehicle using machine learning[J]. Mechanical Systems and Signal Processing, 2021, 160(1):1–10.
Yy A, Yu S, Ds A, et al. A wheel wear prediction model of non-Hertzian wheel-rail contact considering wheelset yaw:Comparison between simulated and field test results[J]. Wear, 2021, 15(1):474–485.
Kvarda D, Galas R, Omasta M, et al. Asperity-based model for prediction of traction in water-contaminated wheel-rail contact[J]. Tribology International, 2021, 157(1):106900–106911.
Maiba I, Glazunov D, Maiba V. Special purpose composite materials for wheel-rail contact[J]. IOP Conference Series: Materials Science and Engineering, 2020, 709(3):033013–033019.
Wang Huan, Yang Guangwu, Ke Xin, et al. Study on the Influence of Geometric Parameters on Load Introduction Factor of Bolted Joints[J]. Computer Simulation, 2020, 37(2):251–257.
Liu P, Yang S, Liu Y. Full-scale test and numerical simulation of wheelset-gear box vibration excited by wheel polygon wear and track irregularity[J]. Mechanical Systems and Signal Processing, 2022, 167(1):108515–108529.
Li S. Correlation analysis method of track irregularity indexes[J]. IOP Conference Series Earth and Environmental Science, 2021, 638(1):012071–012082.
