数字孪生驱动多算法自适应选择的 空间电源系统故障检测

针对积累的空间电源系统遥测数据中故障数据不准确且不全面,进而导致地面长管系统很难根据实际发生的故障数据 综合选择和评估故障检测模型有效性的问题,本文重点开展孪生数据驱动的空间电源系统故障检测模型优化选择方法研究。 在充分分析电源系统组成、工作原理以及输入输出关系的基础上,利用 Simulink 构建航天器电源系统各组成单元的数字孪生模 型,并结合故障机理分析在孪生模型中注入典型的故障,丰富故障数据种类及数量,基于孪生数据实现多种故障检测模型有效 性的评估。 实验表明,基于此框架产生的孪生数据与实测数据相似性达 90%以上,可进行 6 种典型故障模式的注入,可对故障 检测模型的阶跃型以及渐变型故障的检测能力进行有效评估,此种方法的研究可有效服务于实际的地面长管系统,为合理的故 障检测模型的选择提供重要的模型与数据基础。

Digital twin-driven multi-algorithms adaptive selection for fault detection of space power system

In view of the inaccurate and incomplete fault data in the accumulated telemetry data of space power system, it is difficult for the ground long-time management system to comprehensively select and evaluate the effectiveness of fault detection model according to actual fault data. This paper focuses on the research on the optimal selection method of twin data-driven fault detection model for space power system. Based on the full analysis of the composition, working principle and input-output relationship of the power system, the digital twin model of each component unit of the spacecraft power system is constructed by Simulink. Combined with the analysis of fault mechanism, typical faults are injected into the twin model to enrich the types and quantity of fault data, and the effectiveness of various fault detection models is evaluated based on the twin data. Experiments show that the twin data generated based on this framework are more than 90%, which is similar to the real telemetry data, and six typical failure modes can be injected, where the step-type and gradient-type fault detection ability of the fault detection model can be effectively evaluated. The research of this method can effectively serve the actual ground long-time management system and provide an important model and data basis for the selection of effective fault detection model.