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Integrating ISHM with Flight Avionics Architectures for Cyber-Physical Space Systems, Phase I

Completed Technology Project

Project Introduction

Autonomous, avionic and robotic systems are used in a variety of applications including launch vehicles, robotic precursor platforms, etc. Most avionic innovations are based on software-embedded systems, and this has resulted in an increase in the number of interactions (coupling) among heterogeneous subsystems. Avionic systems degrade in performance due to gradual development of anomalies and unanticipated failures ranging from issues affecting a single hardware or software subsystem to issues occurring as a result of coupling among multiple subsystems. In addition, system usage and operating conditions may lead to different failure modes necessating multiple recovery procedures possibly causing conflicts and deadlocks among recovery steps. QSI intends to address these challenges by leveraging the current capabilities of model-based fault management and supportability solutions of TEAMS to efficiently sequence individual steps within each procedure, including adding/deleting steps, and resolve conflicts and deadlocks in recovery procedures. TEAMS-RT, the real-time inference engine, has multiple fault diagnosis capability built-in. Additionally, TEAMS-RDS (TEAMS-remote diagnostic server) already exploits commonalities among test steps during guided troubleshooting, where each test is represented as a chain of pre-setup, post-setup and action nodes with Do and Undo steps interspersed. The proposed effort will extend this to more general digraphs of test and recovery/repair procedures and also embed this capability in a solution linked to enhanced TEAMS-RT for automated /crew-initiated recovery and resolution of conflicts and deadlocks in recovery procedures. This proposal aims to enhance QSI?s existing probabilistic inference engine to handle multiple, intermittent and coupled failure scenarios and developing an ISHM response engine module that dynamically assembles feasible and near-optimal recovery procedures to handle multiple failure scenarios. More »

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