Spare parts management is a critical aspect of high-speed train health management, playing a vital role in maximizing in-service time and minimizing maintenance costs. However, traditional spare parts management methods, which rely solely on historical experience and suggest spare parts quantities or ratios in equipment manuals, often lack practicality and fail to meet real-world demands. To address these limitations, this paper proposes a performance prediction-based spare parts management strategy for high-speed trains. The strategy comprises three main components. First, a performance degradation model is developed using performance evaluation results to define a performance degradation envelope. Next, the required quantity or ratio of spare parts for multiple devices in different performance states is determined using the expected performance score method. Finally, the timing of spare parts orders is scientifically optimized by accounting for production and transportation lead times. To demonstrate the effectiveness of the proposed strategy, we conducted experiments using the spare parts management of a specific high-speed train running gear as a case study and compared it with existing spare parts management methods.

The safe operation of high-speed rail running gear is crucial, as fault diagnosis can effectively prevent potential risks and ensure the smooth operation of the train. The Belief Rule Base (BRB) method has demonstrated excellent performance in complex system modeling. However, during the optimization process, BRB may lead to a "combinatorial explosion" of rules within the model, resulting in a loss of model interpretability and an increase in complexity. To address this, a Multidimensional Belief Rule Base (MBRB) fault diagnosis method is proposed. By optimizing the structure and parameters, the interpretability of the model is enhanced, and its complexity is reduced. Specifically, the model inputs are decomposed into multiple dimensions for analysis, and then the MBRB rules are updated using the Projection Covariance Matrix Adaption Evolution Strategy (P-CMA-ES), increasing the model’s interpretability and accuracy. Finally, the effectiveness of this method is validated through an example of high-speed rail running gear.

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.