Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring Systems
Abstract
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.
- Authors:
-
[1];
- Wichita State University, Wichita, KS 67260, USA, Department of Aerospace Engineering, 1845 Fairmount, Wichita, KS 67226, USA
- Publication Date:
- Research Org.:
- Wichita State Univ., Wichita, KS (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- OSTI Identifier:
- 1227781
- Alternate Identifier(s):
- OSTI ID: 1626215
- Grant/Contract Number:
- DOE DE-FG36-08GO88149; FG36-08GO88149
- Resource Type:
- Published Article
- Journal Name:
- The Scientific World Journal (Online)
- Additional Journal Information:
- Journal Name: The Scientific World Journal (Online) Journal Volume: 2013; Journal ID: ISSN 1537-744X
- Publisher:
- Hindawi Publishing Corporation
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 42 ENGINEERING
Citation Formats
Kral, Zachary, Horn, Walter, and Steck, James. Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring Systems. United Kingdom: N. p., 2013.
Web. doi:10.1155/2013/823603.
Kral, Zachary, Horn, Walter, & Steck, James. Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring Systems. United Kingdom. https://doi.org/10.1155/2013/823603
Kral, Zachary, Horn, Walter, and Steck, James. Tue .
"Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring Systems". United Kingdom. https://doi.org/10.1155/2013/823603.
@article{osti_1227781,
title = {Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring Systems},
author = {Kral, Zachary and Horn, Walter and Steck, James},
abstractNote = {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.},
doi = {10.1155/2013/823603},
journal = {The Scientific World Journal (Online)},
number = ,
volume = 2013,
place = {United Kingdom},
year = {Tue Jan 01 00:00:00 EST 2013},
month = {Tue Jan 01 00:00:00 EST 2013}
}
https://doi.org/10.1155/2013/823603
Search WorldCat to find libraries that may hold this journalWeb of Science
Works referenced in this record:
Structural health monitoring using continuous sensors and neural network analysis
journal, August 2006
- Lee, Jong Won; Kirikera, Goutham R.; Kang, Inpil
- Smart Materials and Structures, Vol. 15, Issue 5
Neural-Network-Based System for Novel Fault Detection in Rotating Machinery
journal, August 2004
- Crupi, V.; Guglielmino, E.; Milazzo, G.
- Journal of Vibration and Control, Vol. 10, Issue 8
Acoustic emission for fatigue damage characterization in metal plates
journal, March 2011
- Aggelis, D. G.; Kordatos, E. Z.; Matikas, T. E.
- Mechanics Research Communications, Vol. 38, Issue 2
Initial evaluation of an active/passive structural neural system for health monitoring of composite materials
journal, August 2006
- Kirikera, G. R.; Lee, J. W.; Schulz, M. J.
- Smart Materials and Structures, Vol. 15, Issue 5