KEYWORDS: Satellites, Structural health monitoring, Space operations, Data modeling, Visual process modeling, Sensors, Satellite communications, 3D displays, Defense and security, Aluminum
The Air Force Research Laboratory/Space Vehicles Directorate (AFRL/RV) is developing Structural Health Monitoring
(SHM) technologies in support of the Department of Defense's Operationally Responsive Space (ORS) initiative. Such
technologies will significantly reduce the amount of time and effort required to assess a satellite's structural surety.
Although SHM development efforts abound, ORS drives unique requirements on the development of these SHM
systems. This paper describes several technology development efforts, aimed at solving those technical issues unique to
an ORS-focused SHM system, as well as how the SHM system could be implemented within the structural verification
process of a Responsive satellite.
KEYWORDS: Autoregressive models, Ultrasonics, Structural health monitoring, Microsoft Foundation Class Library, Algorithm development, Signal processing, Data modeling, Satellites, Sensors, Performance modeling
The Operationally Responsive Space (ORS) strategy hinges, in part, on realizing technologies which can facilitate
the rapid deployment of satellites. Presently, preflight qualification testing and vehicle integration processes
are time consumptive and pose as two significant hurdles which must be overcome to effectively enhance US
space asset deployment responsiveness. There is a growing demand for innovative embedded Structural Health
Monitoring (SHM) technologies which can be seamlessly incorporated onto payload hardware and function in
parallel with satellite construction to mitigate lengthy preflight checkout procedures. In this effort our work is
focused on the development of a joint connectivity monitoring algorithm which can detect, locate, and assess
preload in bolted joint assemblies. Our technology leverages inexpensive, lightweight, flexible thin-film macro-fiber composite (MFC) sensor/actuators with a novel online, data-driven signal processing algorithm. This
algorithm inherently relies upon Chaotic Guided Ultrasonic Waves (CGUW) and a novel cross-prediction error
classification technique. The efficacy of the monitoring algorithm is evaluated through a series of numerical
simulations and experimentally in two test configurations. We conclude with a discussion surrounding further
development of this approach into a commercial product as a real-time flight readiness indicator.
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