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Development of an artificial neural network damage detection module for a structural health monitoring system
Kral, Zachary Tyler
Kral, Zachary Tyler
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2009-05
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Abstract
Aircraft, wind turbines, or space stations are expected to remain in service well beyond their designed
performance lifetime. Consequently, maintenance is an important issue for aircraft or aerospace structures. This is
accomplished through inspecting for damage at scheduled times and replacing damaged parts before failure. Ground
inspections of aircraft, even using simple nondestructive testing techniques, generally require the aircraft be pulled
from operation so that its components can be inspected for damage. Structural components are replaced if sufficient
damage is found. Research is underway to develop a structural health monitoring (SHM) system as a means to
improve the current maintenance routine. This system would consist of an array of sensors and associated analysis
codes which would scan for damage in-flight and perform real-time damage analysis of an aircraft’s structure. If
damage is recognized long before failure occurs, then a damage tolerance and prognostic assessment could be
implemented, allowing for a determination of the remaining life of components. The current method of inspecting
aircraft, consisting of ground inspections for damage after a set number of flight hours, works well from an aircraft
safety point of view. However, an in-flight SHM system would allow for better use of components, as specific
lifetimes could be determined; and, could be less costly, since an SHM system could be embedded into the aircraft
structure, thereby reducing or eliminating the need to tear down the aircraft to scan for damage during the ground
inspection and would ultimately lead to fewer required ground inspections.
General theory for material mechanics, fracture mechanics, waveform theory, and artificial neural networks are
presented in this paper. Among these, a simple triangulation method is developed to locate a crack tip position and a
procedure of combining the theory of fracture mechanics with waveform theories is introduced. These components
were used collectively in a series of experiments to investigate the possibility of using them in a future SHM system.
Flat aluminum panels, similar in thickness to those found in many aerospace structures, were subjected to
increasing static loading during laboratory tests. As the load increased, a designed crack in the panel increased in
size, releasing strain waves into the material. These waves were then detected by acoustic emission sensors, and
artificial neural networks were implemented to analyze the received strain waves. From a feed-forward neural
network, the crack length was approximated.
Next, similar aluminum panels were placed in a simply supported beam configuration with ultrasonic actuators
attached at various positions. These actuators created multiple point source locations, which was received by
multiple acoustic emission sensors. The location of the source was calculated by both triangulation method and an
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artificial neural network. A theory of plastic zone interference with strain waves released from crack tip extension
was introduced and shown as a possibility during the analysis of a final experiment. Sensors placed behind the crack
front were observed to detect waves with smaller amplitudes than the sensors placed in locations in front of the
crack during crack extension due to increasing, pseudo-static applied load. The effect of the acoustic emission
sensor placement relative to crack tip growth detections was determined to be possibly integrated with an artificial
neural network in future research.
Experiments were conducted to determine the crack length and location, using artificial neural network analyses
of acoustic emission signals. Artificial neural networks were developed which were trained with an existing dataset
of crack properties. These neural networks were applied to new situations that were not part of the training dataset.
The approximated crack growth of the artificial neural networks was around 10% shorter than the actual measured
crack growth length for an extension of around 0.8 in. Finally, some SHM analysis systems were proposed, based
on the conclusions made in the experiments. The artificial neural networks performed well at approximating both the
crack extension and location, using acoustic emission detections. These artificial neural networks in combination
with an acoustic emission system are reasonable candidate for the initial stages of a feasible structural health
monitoring system to determine the location and severity of structural damage within an aerospace structure.
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Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Aerospace Engineering
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Wichita State University