Prognostics for Autonomous Deep-Space Habitat Health Management under Multiple Unknown Failure Modes
The framework proposes an unsupervised method for predicting Remaining Useful Life (RUL) in deep-space habitats. It uses unlabeled sensor data to simultaneously identify unknown failure modes and select the most informative sensors for each mode offline, followed by an online system that diagnoses the active failure and predicts RUL.