Sensor-fusion based Prognostics for Deep-space Habitats Exhibiting Multiple Unlabeled Failure Modes
This addresses the challenge of autonomous maintenance in deep-space habitats, where failure modes are unknown and labels are unavailable, representing an incremental advance in prognostics methods.
The paper tackled the problem of predicting remaining useful life for deep-space habitats with multiple unlabeled failure modes by proposing an unsupervised prognostics framework that jointly identifies latent failure modes and selects informative sensors, demonstrating improved accuracy and interpretability on simulated and real-world datasets.
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 a novel 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.