Dynamic reliability evaluation of superconducting electrodynamic maglev-turnout coupled system with multiple stochastic factors: An enhanced PDEM framework with initial condition reconstruction

Abstract

Safe and stable passage of superconducting electrodynamic suspension (EDS) maglev trains through turnout zones is critical to achieving higher operational speeds. Current research lacks systematic analysis of time-varying coupling dynamics in turnout sections and struggles to quantify time-dependent reliability under combined multi-source stochastic disturbances, including track irregularities, vehicle parameters, and turnout structural properties. To resolve these issues, this paper proposes a dynamic reliability evaluation method for the stochastic coupled system of superconducting EDS maglev train and turnout (DRE-EDSTC). The framework integrates the probability density evolution method (PDEM), an improved initial-condition reconstruction technique, and an absorbing boundary method. An adaptive initial-value reconstruction approach based on Gaussian kernel density estimation is adopted to address poor differentiability and low distribution accuracy in traditional methods. Combined with the vehicle-turnout stochastic vibration model, it accurately captures the full-time probability evolution of levitation and guidance air gaps, improving safety risk assessment. Quantitative relationships between negotiating speed and lateral/vertical dynamic reliability are established, with air-gap reliability evolution of the MLX01 train analyzed at 90 – 140 km/h for comprehensive safety evaluation. This study provides theoretical and technical support for the engineering application and commercialization of superconducting EDS maglev systems.

Keywords

absorbing boundary method / dynamic reliability / probability density evolution method / superconducting electrodynamic maglev / stochastic vibration / vehicle-turnout coupled system

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