An Evidential Reasoning Assessment Method Based on Multidimensional Fault Conclusion

The running gear mechanism is a critical component of high-speed trains, essential for maintaining safety and stability. Malfunctions in the running gear can have severe consequences, making it imperative to assess its condition accurately. Such assessments provide insights into the current operational status, facilitating timely maintenance and ensuring the reliable and safe operation of high-speed trains. Traditional evidential reasoning models for assessing the health of running gear typically require the integration of multiple characteristic indicators, which are often challenging to obtain and may lack comprehensiveness. To address these challenges, this paper introduces a novel assessment model that combines evidential reasoning with multidimensional fault conclusions. This model synthesizes results from various fault diagnoses to establish a comprehensive health indicator system for the running gear. The diagnostic outcomes serve as inputs to the model, which then assesses the overall health status of the running gear system. To address potential inaccuracies in initial model parameters, the covariance matrix adaptation evolution strategy (CMA-ES) algorithm is utilized for parameter optimization. Comparative experiments with alternative methods demonstrate that the proposed model offers superior accuracy and reliability in assessing the health status of high-speed train running gear.