Prognostics for Autonomous Deep-Space Habitat Health Management under Multiple Unknown Failure Modes

arXiv:2411.12159v5 Announce Type: replace
Abstract: 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 health and anticipating failures are essential for safe operations. 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. In practice, these failure modes are often unknown, and the sensors providing useful information may vary across modes, making accurate RUL prediction challenging when 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 has two phases: offline sensor selection and failure mode identification, and online diagnosis and RUL prediction. In the offline phase, failure times are modeled using a mixture of Gaussian regressions, and an Expectation-Maximization algorithm simultaneously clusters degradation trajectories and selects mode-specific sensors. In the online phase, low-dimensional features from selected sensors diagnose the active failure mode and predict RUL through a weighted functional regression model. The framework is evaluated on a simulated dataset capturing key telemetry challenges in DSH systems and on the NASA C-MAPSS benchmark. Results show improved prediction accuracy and clearer identification of informative sensors and failure modes than existing methods.