Italiano (Italian) English (Inglese)
sabato, 16 dicembre 2017

Pubblicazioni

Indietro
Dettagli Pubblicazione
Autori:Daniele Codetta Raiteri
Luigi Portinale
Area Scientifica:Artificial Intelligence
Diagnosis
Uncertain Reasoning
Probabilistic Graphical Models
Dependability and Reliability
Titolo:ARPHA: an FDIR architecture for Autonomous Spacecrafts based on Dynamic Probabilistic Graphical Models
Apparso su:TR-INF-2010-12-04-UNIPMN
Editore:DiSIT, Computer Science Institute, UPO
Anno:2010
Tipo Pubblicazione:Technical Report
URL:http://www.di.unipmn.it...R-INF-2010-12-04-UNIPMN.pdf
Sommario: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 (Verification 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, finally 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 probabilistic graphical models.