Dynamic Bayesian Networks for Fault Detection, Identification, and Recovery in Autonomous Spacecraft

This paper describes how to exploit the modeling features and inference capabilities of dynamic Bayesian networks (DBN), in designing and implementing an innovative approach to fault detection, identification, and recovery (FDIR) for autonomous spacecrafts (e.g., a Mars rover). In particular, issues like partial observability, uncertain system evolution and system-environment interaction, as well as the prediction and mitigation of imminent failures can be naturally addressed by the proposed approach. The DBN framework can augment the modeling and analytical power of standard FDIR methodologies, while still being able to be integrated into the usual system modeling procedures (like, for instance, fault tree analysis). An FDIR cycle composed of the tasks of diagnosis (identification of the current state of the system), prognosis (identification of the future state under the current conditions), and recovery (selection of the best set of actions the autonomous system can perform, in order to avoid critical situations) is introduced and characterized through a DBN model. In particular, by considering the execution of recovery actions in response to either a current or a future abnormal situation, both reactive as well as preventive recovery can be addressed respectively. The proposed approach has been implemented in an on-board software architecture called Anomaly resolution and prognostic health management for autonomy (ARPHA), realized during the VERIFIM study funded by the European Space Agency and jointly performed with Thales/Alenia Italy. We report on some of the results obtained by performing a case study concerning the FDIR analysis of the power supply system of the ExoMars rover, by considering different anomalous and failure simulated scenarios; we conclude that ARPHA is able to properly detect and deal with the simulated problems.