There exists a need to remotely monitor the structural integrity of
large space structures without costly manned missions. This work
focused on further damage detection characterization of the Air
Force Institute of Technology's Flexible Truss Experiment (FTE).
The FTE is intended to be representative of a large space
structure. Several damage detection algorithms were developed and
tested for the FTE using 112 deferent damage conditions and the one
undamaged condition. The algorithms were trained using two
frequency response functions (FRFs) from each damage case and then
tested using the same two FRFs, but from newly acquired data. A
data reduction technique from the field of speech recognition was
adapted for this damage detection application. The data was reduced
by greater than an order of magnitude, via a discrete,
point-by-point, integration process in both training and testing.
As shifts in both the resonant and anti-resonant frequencies were
caused by the damage, another damage detection algorithm was
developed that extracted the resonant and anti-resonant frequencies
from the two FRFs. This algorithm vectorized the frequencies of the
peaks and valleys in the FRFs for comparison.
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