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Prediction of Failure Rate of Metro Vehicle Bogie Based on Neural Network

  • Xiuqi Wang
  • Yong QinEmail author
  • Yong Fu
  • Meng Ye
Conference paper
  • 15 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 617)

Abstract

Metro train bogie system is located between the car body and the track, which is one of the key subsystems to ensure the safety of train operation. As a complex system, bogie system is composed of many components, once the failure happened, it would impact the normal operation of the whole train. In order to better predict the failure rate of bogie system, radial basis function (RBF) neural network is introduced to predict the failure rate of the whole system through the fault data of bogie components, and genetic algorithm is used to optimize the model. Experimental results showed that the proposed method can accurately predict the bogie failure rate, and can be used as a system-level reliability prediction method, providing a data basis for later system improvement and optimization.

Keywords

Bogie Genetic algorithm RBF neural network Prediction of failure rate 

Notes

Acknowledgements

The authors gratefully acknowledge the financial supports for this research from National Natural Science Foundation of China (61833002).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.National Key Laboratory of Rail Control and Safety of Beijing Jiaotong UniversityBeijingChina
  2. 2.Beijing Engineering Research Center of Urban Traffic Information Intelligent Sensing and Service TechnologiesBeijingChina

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