Deep-space habitats are complex systems that must operate autonomously over extended durations without ground-based maintenance. These systems are vulnerable to multiple, often unknown, failure modes that affect different subsystems and sensors in mode-specific ways. Developing accurate remaining useful life (RUL) prognostics is challenging, especially when failure labels are unavailable and sensor relevance varies by failure mode. In this paper, we propose an unsupervised prognostics framework that jointly identifies latent failure modes and selects informative sensors using only unlabeled training data. The methodology consists of two phases. In the offline phase, we model system failure times using a mixture of Gaussian regressions and apply an Expectation-Maximization algorithm to cluster degradation trajectories and select mode-specific sensors. In the online phase, we extract low-dimensional features from the selected sensors to diagnose the active failure mode and predict RUL using a weighted regression model. We demonstrate the effectiveness of our approach on a simulated dataset that reflects deep-space telemetry characteristics and on a real-world engine degradation dataset, showing improved accuracy and interpretability over existing methods.

翻译：深空栖息地是必须在无地面维护条件下长期自主运行的复杂系统。这些系统易受多种通常未知的失效模式影响，这些失效模式会以特定方式影响不同子系统和传感器。开发精确的剩余使用寿命预测具有挑战性，特别是在失效标签缺失且传感器相关性随失效模式变化的情况下。本文提出一种无监督预测框架，仅利用未标记训练数据即可联合识别潜在失效模式并选择信息性传感器。该方法包含两个阶段：离线阶段采用高斯回归混合模型对系统失效时间建模，应用期望最大化算法对退化轨迹进行聚类并选择模式特异性传感器；在线阶段从选定传感器提取低维特征以诊断活跃失效模式，并采用加权回归模型预测剩余使用寿命。我们在反映深空遥测特性的模拟数据集和真实发动机退化数据集上验证了方法的有效性，结果表明相较于现有方法，本方法在预测精度和可解释性方面均有提升。