Abstract
This paper presents a Bayesian-MCMC model to assess collector shoes slider degradation under different materials. A Markov Chain Monte Carlo (MCMC) method, based on the Bayesian decision model, is put forward and built up a framework in case of the life cycle of collector shoes under different materials forecast. All of inspection data is gathered from Beijing metro lines, and WinBUGS software are used to predict the slider’s wear rate. Result shows that the difference between the predicted value and the real one is less than 10% of the later one. Consequently, in case of new metro equipment parts, the newest method is able to ensure the safety operation in the metro by providing a valid device to the equipment manufacturers, the maintenance department as well as the purchasing department of the metro equipment.
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Acknowledgements
This work was supported by the Independent Research Project of State Key Lab of Rail Traffic Control & Safety of Beijing Jiaotong University (NO. RCS2017ZJ001).
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Pan, Y., Cai, G., Li, X. (2018). A Bayesian-MCMC Model to Assess Metro Train Collector Shoes Slider Degradation Under Different Materials. 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_35
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DOI: https://doi.org/10.1007/978-981-10-7989-4_35
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