Maintenance is an important issue for aerospace systems, since they are in service beyond their designed lifetime.
This requires scheduled inspections and damage repair before failure. Research is in progress to develop a structural
health monitoring system (SHMS) to improve this maintenance routine. Ultrasonic testing, utilizing a system of
piezoelectric actuators and sensors, is a promising concept Measured wave signals are compared with signals for
previously scanned states. Changes to the signal could be the result of damage to the component. This paper focuses
on analyzing the differences of states, using artificial neural networks. Neural network analysis has the potential of
creating a SHMS of greater ability and processing. Experiments were performed on a thin, flat aluminum panel.
Ultrasonic actuators and sensors were installed and a baseline scan was performed on the undamaged panel.
Simulated damage was introduced in specific areas, and scans were conducted for several damaged states. Neural
networks were created to assess the changing conditions of the panel. The system was later tested on a lap joint
specimen to confirm the abilities of the neural network. This form of analysis performed well at locating and
quantifying areas of change within the structure. The neural network performance indicated that it has a role in the
SHMS of aerospace structures.