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Online prediction model for wheel wear considering track flexibility

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Abstract

The objective of this study is to develop a new online model for wheel wear that takes into account the track flexibility. The proposed model consists of two parts that interact with each other, namely, (a) a locomotive/track coupled dynamics model considering the track flexibility, which is validated by field measurement results, and (b) a model for the wear estimation. The wheel wear prediction model can be employed in online solutions rather than in post-processing. The effect of including the track flexibility on the wear estimation is investigated by comparing the results with those obtained for a rigid track. Moreover, the effect of the wheel profile updating strategy on the wheel wear is also examined. The simulation results indicate that the track flexibility cannot be neglected for the wheel wear prediction. The wear predicted with the rigid track model is generally larger than that predicted with the flexible track model. The strategy of maintaining unchanged wheel profiles during the dynamic simulation coincides with the online updating strategy in terms of the predicted wear.

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Acknowledgements

The present work is supported by the National Natural Science Foundation of China (No. U1361117), the Program for Building the Scientific Research Innovation Team in Colleges and Universities in Sichuan Province (No. 17TD0040), the Scientific Research Foundation of the State Key Laboratory of Traction Power of Southwest Jiaotong University (No. 2017TPL_T05) and the Opening Project of The State Key Laboratory of Heavy Duty AC Drive Electric Locomotive Systems Integration (No. 2017ZJKF01). The authors would like to thank CRRC Zhuzhou Locomotive Co., Ltd. for providing the measurement results of locomotive dynamics performance to validate the locomotive/track coupled dynamics model.

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Authors and Affiliations

  1. State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, People’s Republic of China

    Gongquan Tao, Dexiang Ren, Zefeng Wen & Xuesong Jin

  2. The State Key Laboratory of Heavy Duty AC Drive Electric Locomotive Systems Integration, Zhuzhou, People’s Republic of China

    Linfeng Wang

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  1. Gongquan Tao
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  2. Dexiang Ren
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  3. Linfeng Wang
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  4. Zefeng Wen
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  5. Xuesong Jin
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Correspondence to Zefeng Wen.

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Tao, G., Ren, D., Wang, L. et al. Online prediction model for wheel wear considering track flexibility. Multibody Syst Dyn 44, 313–334 (2018). https://doi.org/10.1007/s11044-018-09633-5

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