Linear damage location using fiber optic acoustic emission sensors for structure health monitoring

2011 ◽  
Author(s):  
Tao Fu ◽  
Zhichun Zhang ◽  
Zaiwen Lin ◽  
Zhenhua Yao ◽  
Jinsong Leng
Keyword(s):  
Acoustic Emission ◽  
Health Monitoring ◽  
Fiber Optic ◽  
Structure Health Monitoring ◽  
Damage Location ◽  
Acoustic Emission Sensors

Fiber-optic intrinsic distributed acoustic emission sensor for large structure health monitoring

2009 ◽  
Vol 34 (12) ◽  
pp. 1858 ◽  
Author(s):  
Sheng Liang ◽  
Chunxi Zhang ◽  
Wentai Lin ◽  
Lijing Li ◽  
Chen Li ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Health Monitoring ◽  
Fiber Optic ◽  
Large Structure ◽  
Structure Health Monitoring ◽  
Acoustic Emission Sensor

Fiber-optic acoustic emission sensors with dual Fabry-Perot interferometric cavities

2015 ◽  
Vol 23 (11) ◽  
pp. 3069-3076
Author(s):  
赵江海 ZHAO Jiang-hai ◽  
章小建 ZHANG Xiao-jian
Keyword(s):  
Acoustic Emission ◽  
Fiber Optic ◽  
Fabry Perot ◽  
Acoustic Emission Sensors

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

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Zachary Kral ◽  
Walter Horn ◽  
James Steck
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Artificial Neural Networks ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Health ◽  
Aerospace Systems ◽  
Acoustic Emission Sensors ◽  
Artificial Neural ◽  
The Difference

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.

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