Accurate on-orbit reliability prediction for satellite electronics is often hindered by limited data availability, varying operational conditions, and considerable unit-to-unit variability. To overcome these obstacles, this paper proposes a novel integrated online reliability prediction framework. The main contributions are twofold. First, a Wiener process-based degradation model is developed, incorporating a generalized Arrhenius link function, individual random effects, and spatial correlations among adjacent units. A customized maximum likelihood estimation method is further devised to facilitate efficient and accurate parameter inference. Second, a two-stage active learning sampling scheme is designed to adaptively enhance prediction accuracy. This strategy initially selects representative units based on spatial configuration, and subsequently determines optimal sampling times using a comprehensive criterion that balances unit-specific information, model uncertainty, and degradation dynamics. Numerical experiments and a practical case study from the Tiangong space station demonstrate that the proposed method markedly improves reliability prediction accuracy while significantly reducing data requirements, offering an efficient solution for the prognostic and health management of complex satellite electronic systems.

翻译：卫星电子设备的在轨可靠性预测常受限于数据可用性不足、运行条件多变及显著的单元间差异。为克服这些障碍，本文提出一种新型集成式在线可靠性预测框架。主要贡献包含两个方面：首先，建立了基于维纳过程的退化模型，该模型融合了广义阿伦尼乌斯链接函数、个体随机效应以及相邻单元间的空间相关性。进一步设计了定制化的最大似然估计方法，以实现高效准确的参数推断。其次，提出两阶段主动学习采样策略以自适应提升预测精度。该策略首先依据空间构型选择代表性单元，继而通过综合权衡单元特异性信息、模型不确定性与退化动力学的优化准则确定最佳采样时机。数值实验及天宫空间站的实际案例研究表明，所提方法在显著降低数据需求的同时，能有效提升可靠性预测精度，为复杂卫星电子系统的预测与健康管理提供了高效解决方案。