ARPHA: an FDIR architecture for Autonomous Spacecrafts based on Dynamic Probabilistic Graphical Models

This paper introduces a formal architecture for on-board diagnosis, prognosis and recovery called ARPHA. ARPHA is designed as part of the ESA/ESTEC study called VERIFIM (Veri\ufb01cation of Failure Impact by Model checking). The goal is to allow the design of an innovative on-board FDIR process for autonomous systems, able to deal with uncertain system/environment interactions, uncertain dynamic system evolution, partial observability and detection of recovery actions taking into account imminent failures. We show how the model needed by ARPHA can be built through a standard fault analysis phase, \ufb01nally producing an extended version of a fault tree called EDFT; we discuss how EDFT can be adopted as a formal language to represent the needed FDIR knowledge, that can be compiled into a corresponding Dynamic Decision Network to be used for the analysis. We also discuss the software architecture we are implementing following this approach, where on-board FDIR can be implemented by exploiting on-line inference based on the junction tree approach typical of probabilisticgraphical models

SAN models of a benchmark on dynamic reliability

This report provides the detailed description of the Stochastic Activity Network (SAN) models appearing in [1] and concerning a benchmark on dynamic reliability taken from the literature

SAN models of communication scenarios inside the Electrical Power System

This report provides all the details about the models and the quantitative results presented in [1], about the simulation of communication scenarios inside the Electrical Power System. In particular, the scenarios deal with the communication between one area control centre and a set of substations in a distribution grid, exchanging commands and signals by means of a redundant communication network. The communication may be affected by threats such as the communication network failure, or intrusions into the communication, causing the loss of commands or signals. The scenarios have been modeled and simulated in form of Stochastic Activity Networks, with the purpose of evaluating the effects of such threats on the communication reliability

Simulating the communication of commands and signals in a distribution grid

The report presents the simulation of communication scenarios involving one area control centre and a set of substations inside a distribution grid of the Electrical Power System. In such scenarios, the communication is affected by threats different from those under exam in [1, 2]; in particular, here, we consider the denial of service attack to the communication network, and the temporary internal failure of a subset of substations. The scenarios have been modeled and simulated in form of Stochastic Activity Networks (SAN); the goal is the evaluation of the impact of the threats, on the communication reliability

A GSPN semantics for Continuous Time Bayesian Networks with Immediate Nodes

In this report we present an extension to Continuous Time Bayesian Networks (CTBN) called Generalized Continuous Time Bayesian Networks (GCTBN). The formalism allows one to model, in addition to continuous time delayed variables (with exponentially distributed transition rates), also non delayed or "immediate" variables, which act as standard chance nodes in a Bayesian Network. This allows the modeling of processes having both a continuous-time temporal component and an immediate (i.e. non-delayed) component capturing the logical/probabilistic interactions among the model\u2019s variables. The usefulness of this kind of model is discussed through an example concerning the reliability of a simple component-based system. A semantic model of GCTBNs, based on the formalism of Generalized Stochastic Petri Nets (GSPN) is outlined, whose purpose is twofold: to provide a well-de\ufb01ned semantics for GCTBNs in terms of the underlying stochastic process, and to provide an actual mean to perform inference (both prediction and smoothing) on GCTBNs. The example case study is then used, in order to highlight the exploitation of GSPN analysis for posterior probability computation on the GCTBN model