Date of Original Version

September 2011



Abstract or Description

Modern aircraft — both piloted fly-by-wire commercial aircraft as well as UAVs — more and more depend on highly complex safety critical software systems with many sensors and computer-controlled actuators. Despite careful design and V&V of the software, severe incidents have happened due to malfunctioning software.

In this paper, we discuss the use of Bayesian networks to monitor the health of the on-board software and sensor system, and to perform advanced on-board diagnostic reasoning. We focus on the development of reliable and robust health models for combined software and sensor systems, with application to guidance, navigation, and control (GN&C). Our Bayesian network-based approach is illustrated for a simplified GN&C system implemented using the open source real-time operating system SEK/Trampoline. We show, using scenarios with injected faults, that our approach is able to detect and diagnose faults in software and sensor systems.



Published In

Annual conference of the prognostics and health management society 2011 (PHM-11).


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