Study on the evaluation method of high speed train based on improved fuzzy hierarchy analysis
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Research on in-service evaluation method of profile matching performance and opportunity maintenance strategy for wheel/rail of high-speed train

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    Abstract:

    【AIMS】 In view of the lack of comprehensive evaluation system for high-speed train wheel and rail optimization scheme, this study aims to develop an evaluation method based on improved fuzzy hierarchy analysis method to determine the most appropriate equivalent conicity scheme and ensure the smooth operation of the train.【Methods】Firstly, in the light of the main kinetic factors affecting the selection of equivalent conicity, 14 evaluation indexes were selected to construct the index decomposition model, and the improved three-scale hierarchy analysis method was applied to determine the weight of each index. Subsequently, three different working conditions with equivalent conicity were established in the dynamics software Simpack, and the underlying index values were calculated and dimensionless to determine the fuzzy evaluation membership matrix, and the fuzzy theory system evaluation model was constructed to obtain the comprehensive evaluation results of the three schemes.【Results】The comprehensive evaluation results show that the scores of the three equivalent conicity schemes are: M1:26.2%, M2:17.9%, and M3:16.2%, from which M1 was determined as the optimal choice. By calculating the kinetic simulation of the two groups and observing the intuitive simulation results of the post-processing module, the results are consistent with the conclusion of the comprehensive evaluation system, and then the reliability of the evaluation method is verified.【Conclusion】The method of equivalent conicity evaluation of high-speed train wheel and rail based on improved fuzzy hierarchy analysis proposed in this study is effective, which can provide scientific basis for the optimization of equivalent conicity scheme of high-speed trains and ensure the stability and safety of trains.

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History
  • Received:March 16,2024
  • Revised:March 16,2024
  • Adopted:May 06,2024
  • Online: June 14,2024
  • Published:
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