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Proceedings Paper

Neural network approach of active ultrasonic signals for structural health monitoring analysis
Author(s): Zachary Kral; Walter Horn; James Steck
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Paper Abstract

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.

Paper Details

Date Published: 8 April 2009
PDF: 11 pages
Proc. SPIE 7295, Health Monitoring of Structural and Biological Systems 2009, 729507 (8 April 2009); doi: 10.1117/12.815503
Show Author Affiliations
Zachary Kral, Wichita State Univ. (United States)
Walter Horn, Wichita State Univ. (United States)
James Steck, Wichita State Univ. (United States)


Published in SPIE Proceedings Vol. 7295:
Health Monitoring of Structural and Biological Systems 2009
Tribikram Kundu, Editor(s)

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