Reliability Assessment of High-Speed Train Gearbox Based on Digital Twin and WHO-WPHM

The gearbox is essential for power transmission in high-speed trains, and its reliability directly impacts operational safety. Accurate monitoring data and effective assessment methods are crucial for accurately assessing its reliability. This study is based on digital twin (DT) technology, precisely deploying virtual sensors to collect vibration data from critical measurement points accurately. By integrating the Wild Horse Optimizer (WHO) and the Weibull Proportional Hazards Model (WPHM), it achieved reliability assessment for a high-speed train gearbox. First, a DT framework for the high-speed train gearbox was established. Taking the gear pair, a critical power transmission component in the gearbox, as an example, a DT model of the gear pair was built on Ansys Twin Builder, virtual sensors were deployed at critical measurement points, and vibration acceleration data was collected. Then, a WPHM reliability assessment model was established, and the WHO was used to estimate and optimize the WPHM parameters. Finally, the response covariates reduced by the Local Tangent Space Alignment (LTSA) were used as model inputs, and the WPHM was applied to assess the reliability of critical parts based on the collected data. The web-deployed DT model was delivered within 13 s. This achieved a simulation acceleration factor of 2.35 × 10⁴, compared to traditional methods. The number of iterations for the WOA was reduced by 62.9% compared to the WHO and by 48.1% compared to the HHO. The reliability assessment results align with the actual operating mileage status of the gear pair, thus validating the effectiveness and feasibility of this method.