Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring Systems
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 r...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/823603 |
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| author | Zachary Kral Walter Horn James Steck |
| author_facet | Zachary Kral Walter Horn James Steck |
| author_sort | Zachary Kral |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ead830b602844ae0b7dd9c1a4708ae88 |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-ead830b602844ae0b7dd9c1a4708ae882025-02-03T01:10:04ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/823603823603Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring SystemsZachary Kral0Walter Horn1James Steck2Wichita State University, Wichita, KS 67260, USAWichita State University, Wichita, KS 67260, USAWichita State University, Wichita, KS 67260, USAAerospace 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.http://dx.doi.org/10.1155/2013/823603 |
| spellingShingle | Zachary Kral Walter Horn James Steck Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring Systems The Scientific World Journal |
| title | Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring Systems |
| title_full | Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring Systems |
| title_fullStr | Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring Systems |
| title_full_unstemmed | Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring Systems |
| title_short | Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring Systems |
| title_sort | crack propagation analysis using acoustic emission sensors for structural health monitoring systems |
| url | http://dx.doi.org/10.1155/2013/823603 |
| work_keys_str_mv | AT zacharykral crackpropagationanalysisusingacousticemissionsensorsforstructuralhealthmonitoringsystems AT walterhorn crackpropagationanalysisusingacousticemissionsensorsforstructuralhealthmonitoringsystems AT jamessteck crackpropagationanalysisusingacousticemissionsensorsforstructuralhealthmonitoringsystems |