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Journal of Vibration Testing and System Dynamics 7(3) (2023) 307--326 | DOI:10.5890/JVTSD.2023.09.005

Anni Zhao, Jian-Qiao Sun

Department of Mechanical Engineering, University of California, Merced, CA 95343, USA

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Abstract

Active controls have been used to enhance the stability of high speed trains against hunting instability. The control system uses motion measurements of the train for decision making. These measurements can also be used to estimate wear of the wheel due to dynamic interactions with the rail. In this paper, we present an approach that makes use of the well-known extended state estimator to estimate interaction forces between the wheel and rail from motion measurements. Archard's wear model is adopted to compute the damage of the wheel. To take advantages of motion measurements further, we treat Archard's wear model as a surrogate to generate a large set of wear data from simulated motions. This is valuable because it is expensive and time consuming to collect real data of wheel wear. We develop a neural networks model to directly link train motions with the wheel wear. With the neural networks model, we can then predict the wheel wear from motion measurements of high speed train in service. It is expected that the neural networks wear model can help engineers to develop more effective maintenance schedule.

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