Deep-space habitats (DSHs) are safety-critical systems that must operate autonomously for long periods, often beyond the reach of ground-based maintenance or expert intervention. Monitoring system health and anticipating failures are therefore essential. Prognostics based on remaining useful life (RUL) prediction support this goal by estimating how long a subsystem can operate before failure. Critical DSH subsystems, including environmental control and life support, power generation, and thermal control, are monitored by many sensors and can degrade through multiple failure modes. These failure modes are often unknown, and informative sensors may vary across modes, making accurate RUL prediction challenging when historical failure data are unlabeled. We propose an unsupervised prognostics framework for RUL prediction that jointly identifies latent failure modes and selects informative sensors using unlabeled run-to-failure data. The framework consists of two phases: an offline phase, where system failure times are modeled using a mixture of Gaussian regressions and an Expectation-Maximization algorithm to cluster degradation trajectories and select mode-specific sensors, and an online phase for real-time diagnosis and RUL prediction using low-dimensional features and a weighted functional regression model. The approach is validated on simulated DSH telemetry data and the NASA C-MAPSS benchmark, demonstrating improved prediction accuracy and interpretability.

翻译：深空栖息地(DSHs)是安全关键系统，需在长期无人值守情况下自主运行，通常超出地面维护或专家干预范围。因此，系统健康监测与故障预判至关重要。基于剩余使用寿命(RUL)预测的故障预判方法通过估算子系统在失效前的可持续运行时间，为上述目标提供支撑。涵盖环境控制与生命保障、电力生成及热控等关键子系统的DSH由多类传感器监测，可能因多种故障模式而退化。这些故障模式往往未知，且在不同模式下有效传感器存在差异，导致历史故障数据无标注时难以实现精确RUL预测。我们提出一种面向RUL预测的无监督预判框架，利用无标签的运行至失效数据同时识别潜在故障模式并筛选有效传感器。该框架包含两个阶段：离线阶段采用高斯混合回归与期望最大化算法对退化轨迹进行聚类并选取模式特异性传感器，以建模系统失效时间；在线阶段则利用低维特征与加权函数回归模型实现实时诊断与RUL预测。该方法在模拟DSH遥测数据及NASA C-MAPSS基准数据集上得到验证，展现出更优的预测精度与可解释性。