Research on Structural reliability and reliability sensitivity of EMU pantograph

Author:

Feng Zhou,Chang Wu,Zixu Zhang

Abstract

Abstract Due to the uncertainty of the contact state between the pantograph and the power grid when the EMU is running at high speed, as well as the influence of road conditions and environment, the external load applied to the pantograph and the material properties of the pantograph have certain randomness, thus affecting the current collection quality of the pantograph and the service life of the carbon slide plate. Taking a certain type of EMU single arm pantograph as the research object, a three-dimensional model is established, ANSYS Workbench software is used to carry out the finite element analysis to determine the dangerous point and its stress response; the simulation samples of material properties and stress of dangerous points are obtained by using sampling method and test design method based on the principle of normal distribution; the reliability index and reliability of pantograph structure strength are analyzed, based on the random perturbation method and the first-order second- moment method, and using the simulation sample data extracted by Latin hypercube sampling, and the reliability accuracy is verified by Monte Carlo method. Based on the definition of reliability sensitivity, the influence degree of each input variable on the reliability change of pantograph structure is determined as follows: external load > Poisson’s ratio > material density > elastic modulus, which provides reference for improving the reliability, service life and structural optimization design of pantograph

Publisher

IOP Publishing

Subject

General Medicine

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1. Reliability of Pantograph Systems in the High-Speed Train: A Literature Review;Proceedings of the 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2023;2024

2. Dynamic Reliability Assessment Method for a Pantograph System Based on a Multistate T-S Fault Tree, Dynamic Bayesian;Applied Sciences;2023-09-26

3. Deep learning-based methods in structural reliability analysis: a review;Measurement Science and Technology;2023-04-04

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