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The Scientific World Journal
Volume 2013 (2013), Article ID 823603, 13 pages
Research Article

Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring Systems

1Wichita State University, Wichita, KS 67260, USA
2Department of Aerospace Engineering, 1845 Fairmount, Wichita, KS 67226, USA

Received 14 March 2013; Accepted 19 June 2013

Academic Editors: T. E. Matikas and M. R. Woike

Copyright © 2013 Zachary Kral et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Aerospace systems are expected to remain in service well beyond their designed life. Consequently, maintenance is an important issue. A novel method of implementing artificial neural networks and acoustic emission sensors to form a structural health monitoring (SHM) system for aerospace inspection routines was the focus of this research. Simple structural elements, consisting of flat aluminum plates of AL 2024-T3, were subjected to increasing static tensile loading. As the loading increased, designed cracks extended in length, releasing strain waves in the process. Strain wave signals, measured by acoustic emission sensors, were further analyzed in post-processing by artificial neural networks (ANN). Several experiments were performed to determine the severity and location of the crack extensions in the structure. ANNs were trained on a portion of the data acquired by the sensors and the ANNs were then validated with the remaining data. The combination of a system of acoustic emission sensors, and an ANN could determine crack extension accurately. The difference between predicted and actual crack extensions was determined to be between 0.004 in. and 0.015 in. with 95% confidence. These ANNs, coupled with acoustic emission sensors, showed promise for the creation of an SHM system for aerospace systems.