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.

A model-to-model transformation from Dynamic Fault Trees to Stochastic Petri Nets, by means of graph transformation rules, is presented in this paper. Dynamic Fault Trees (DFT) are used for the reliability analysis of complex and large systems and represent by means of gates, how combinations or sequences of component failure events, lead to the failure of the system. DFTs need the state space solution which can be obtained by converting a DFT to a Stochastic Petri Net: this task is expressed by means of graph transformation rules, and is applied to a case of system

A new approach is proposed to include s-dependencies in fault tree (FT) models. With respect to previous techniques, the approach presented in this paper is based on two peculiar powerful features. First, adopting a parameterization technique, referred to as parametric FT (PFT), to fold equal subtrees (or basic events) in order to resort to a more compact FT representation. It is shown that parameterization can be conveniently adopted as well for dynamic gates. Second, PFT can be modularized and each module translated into a high level colored Petri net in the form of a stochastic well-formed net (SWN). SWN generate a lumped Markov chain and the saving in the dimension of the state space can be very substantial with respect to standard (non colored) Petri nets. Translation of PFT modules into SWN has proved to be very flexible, and various kinds of new dependencies can be easily accommodated. In order to exploit this flexibility a new primitive, called repair box, is introduced. A repair box, attached to an event, causes the starting of a repair activity of all the components that failed as the event occurs. In contrast to all the previous FT based models, the addition of repair boxes enables the approach to model cyclic behaviors. The proposed approach as dynamic repairable PFT (DRPFT) was referred to. A tool supporting DRPFT is briefly described and the tool is validated by analyzing a benchmark proposed recently in the literature for quantitative comparison [H. Zhu et al., 2001].