Abstract
Aiming at improving the current unreasonable wheelset re-profiling schedule caused by class-lathing, this paper proposes a re-profiling strategy based on historical wear data. First, the statistical product and service solutions (SPSS) software is used to analyze the interdependency between wheel diameter wear and wheel flange thickness based on the wheel wear data of Guangzhou Metro Line 8. Second, flange thickness wear model based on state transition and wheel diameter wear model based on mathematical statistics are built, and single-stage and multistage planned turns strategies of an individual wheel are built on the two models. Finally, Monte Carlo simulation is used to compare two strategies, and the results show that multistage planned turns strategies can prolong expected life of wheels and effectively save operating cost.
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Acknowledgements
This work is supported by national key R&D program of China (No. 2017YFB 1201102).
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Li, B., Yang, Z., Xing, Z., Gao, X. (2018). Optimization of Wheel Re-profiling Strategy Based on a Statistical Wear Model. In: Jia, L., Qin, Y., Suo, J., Feng, J., Diao, L., An, M. (eds) Proceedings of the 3rd International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2017. EITRT 2017. Lecture Notes in Electrical Engineering, vol 483. Springer, Singapore. https://doi.org/10.1007/978-981-10-7989-4_10
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DOI: https://doi.org/10.1007/978-981-10-7989-4_10
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