Stochastic Dynamics of Suspension System in Maglev Train: Governing Equations for Response Statistics and Reliability

磁悬浮列车 随机振动 悬挂(拓扑) 控制理论(社会学) 概率密度函数 可靠性(半导体) 振动 时域 随机过程 数学 工程类 物理 计算机科学 统计 控制(管理) 纯数学 同伦 功率(物理) 人工智能 电气工程 量子力学 计算机视觉
作者
Wantao Jia,Mingxia Luo,Fei Ni
出处
期刊:International Journal of Structural Stability and Dynamics [World Scientific]
被引量:2
标识
DOI:10.1142/s0219455423501924
摘要

The suspension system of the maglev train will inevitably be disturbed by random factors such as track irregularities, which will cause random vibration of the train and even affect the safety of the train. Therefore, the research on the response and reliability of suspension system under random disturbance is crucial to its safe operation. In this paper, the response and the reliability of a suspension system are investigated using the theory and methods of stochastic dynamics. First, the magnetic gap and vertical velocity of the suspension system are random due to the random disturbance. Thus, the stochastic response is investigated through the probability density function (PDF), which is governed by the Fokker–Planck–Kolmogorov (FPK) equation corresponding to suspension system. And the response statistics of the suspension system under different system parameters and disturbance intensities are analyzed by solving the corresponding FPK equation using the finite difference (FD) method. Second, random disturbance may lead to the vibration amplitude of the suspension system exceeding the safety domain and causing safety incident, which is a reliability problem in stochastic dynamical systems. The probability that response is still in the safety domain at a given time is the reliability function of the suspension system, which is governed by the backward Kolmogorov equation. The time that the response first passes through the safety domain is the first-passage time, and its n-order moment satisfies the generalized Pontryagin equation. Reliability of the suspension system is analyzed by solving these governing equations using the FD method. In addition, the results of the FD method in this paper are verified with those of Monte Carlo (MC) simulation, which shows the correctness of FD method.
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