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Bayesian Software Health Management for Aircraft Guidance, Navigation, and Control

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journal contribution
posted on 2011-09-29, 00:00 authored by Johann M. Schumann, Timmy Mbaya, Ole J. Mengshoel
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.