Date of Award

3-2000

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Aeronautics and Astronautics

First Advisor

Jeffrey Turcotte, PhD

Abstract

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 different 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. Over 99% accuracy was obtained using both the adapted speech recognition method and the resonant and anti-resonant frequencies method. However, only 44% accuracy was achieved by training the resonant and anti-resonant frequencies method with synthetic, Finite Element Model generated data, and testing it with measured data.

AFIT Designator

AFIT-GA-ENY-00M-02

DTIC Accession Number

ADA380344

Comments

The author's vita page is omitted.

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