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Runtime Observer Pairs and Bayesian Network Reasoners On-board FPGAs: Flight-Certifiable System Health Management for Embedded Systems

  • Johannes Geist
  • Kristin Y. Rozier
  • Johann Schumann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8734)

Abstract

Safety-critical systems, like Unmanned Aerial Systems (UAS) that must operate totally autonomously, e.g., to support ground-based emergency services, must also provide assurance they will not endanger human life or property in the air or on the ground. Previously, a theoretical construction for paired synchronous and asynchronous runtime observers with Bayesian reasoning was introduced that demonstrated the ability to handle runtime assurance within the strict operational constraints to which the system must adhere. In this paper, we show how to instantiate and implement temporal logic runtime observers and Bayesian network diagnostic reasoners that use the observers’ outputs, on-board a field-standard Field Programmable Gate Array (FPGA) in a way that satisfies the strict flight operational standards of Realizability, Responsiveness, and Unobtrusiveness. With this type of compositionally constructed diagnostics framework we can develop compact, hierarchical, and highly expressive health management models for efficient, on-board fault detection and system monitoring. We describe an instantiation of our System Health Management (SHM) framework, rt-R2U2, on standard FPGA hardware, which is suitable to be deployed on-board a UAS. We run our system with a full set of real flight data from NASA’s Swift UAS, and highlight a case where our runtime SHM framework would have been able to detect and diagnose a fault from subtle evidence that initially eluded traditional real-time diagnosis procedures.

Keywords

Bayesian Network Temporal Logic Field Programmable Gate Array Linear Temporal Logic Arithmetic Circuit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Johannes Geist
    • 1
  • Kristin Y. Rozier
    • 2
  • Johann Schumann
    • 3
  1. 1.USRA/RIACSMountain ViewUSA
  2. 2.NASA ARCMoffett FieldUSA
  3. 3.SGT, Inc., NASA AmesMoffett FieldUSA

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