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

TL;DR

This paper introduces an unsupervised framework for prognostics in deep-space habitats, enabling accurate remaining useful life prediction despite multiple unknown failure modes and unlabeled data.

Contribution

It proposes a novel two-phase approach combining Gaussian mixture models and functional regression for unsupervised failure mode identification and RUL prediction.

Findings

Validated on simulated DSH data showing improved accuracy

Demonstrated effectiveness on NASA C-MAPSS benchmark

Enhanced interpretability of failure modes and sensor importance

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 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.