
Towards Real-time, On-board, Hardware-supported Sensor and Software Health Management for Unmanned Aerial Systems
Author(s) -
Johann Schumann,
Kristin Yvonne Rozier,
Thomas Reinbacher,
Ole J. Mengshoel,
Timmy Mbaya,
Corey A. Ippolito
Publication year - 2020
Publication title -
international journal of prognostics and health management
Language(s) - English
Resource type - Journals
ISSN - 2153-2648
DOI - 10.36001/ijphm.2015.v6i1.2243
Subject(s) - computer science , embedded system , modular design , software , real time computing , probabilistic logic , field programmable gate array , robustness (evolution) , systems engineering , reliability engineering , engineering , artificial intelligence , biochemistry , chemistry , programming language , gene , operating system
For unmanned aerial systems (UAS) to be successfully deployed and integrated within the national airspace, it is imperative that they possess the capability to effectively complete their missions without compromising the safety of other aircraft, as well as persons and property on the ground. This necessity creates a natural requirement for UAS that can respondto uncertain environmental conditions and emergent failures in real-time, with robustness and resilience close enough to those of manned systems. We introduce a system that meets this requirement with the design of a real-time onboard system health management (SHM) capability to continuously monitor sensors, software, and hardware components. This system can detect and diagnose failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the-fly temporal and Bayesian probabilistic fault diagnosis; and (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software. We call this approach rt-R2U2, a name derived from its requirements. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual flight data from theNASA Swift UAS.