TY - GEN
T1 - A curvature based approach using long-gage fiber optic sensors
AU - Kliewer, Kaitlyn
AU - Glisic, Branko
N1 - Publisher Copyright:
© 2016 SPIE.
PY - 2016
Y1 - 2016
N2 - Fiber Bragg grating (FBG) sensors offer a significant advantage for structural health monitoring due to their ability to simultaneously monitor both static and dynamic strain while being durable, lightweight, capable of multiplexing, and immune to electro-magnetic interference. Drawing upon the benefits of FBG sensors, this research explores the use of a series of long-gage fiber optic sensors for damage detection of a structure through dynamic strain measurements and curvature analysis. Typically structural monitoring relies upon detecting structural changes through frequency and acceleration based analysis. However, curvature and strain based analysis may be a more reliable means for structural monitoring as they show more sensitivity to damage compared to modal parameters such as displacement mode shapes and natural frequency. Additionally, long gage FBG strain sensors offer a promising alternative to traditional dynamic measurement methods as the curvature can be computed directly from the FBG strain measurements without the need for numerical differentiation. Small scale experimental testing was performed using an aluminum beam instrumented with a series of FBG optical fiber sensors. Dynamic strain measurements were obtained as the aluminum beam was subjected to various loading and support conditions. From this, a novel normalized parameter based on the curvature from the dynamic strain measurements has been identified as a potential damage sensitive feature. Theoretical predictions and experimental data were compared and conclusions carried out. The results demonstrated the potential of the novel normalized parameter to facilitate dynamic monitoring at both the local and global scale, thus allowing assessment of the structures health.
AB - Fiber Bragg grating (FBG) sensors offer a significant advantage for structural health monitoring due to their ability to simultaneously monitor both static and dynamic strain while being durable, lightweight, capable of multiplexing, and immune to electro-magnetic interference. Drawing upon the benefits of FBG sensors, this research explores the use of a series of long-gage fiber optic sensors for damage detection of a structure through dynamic strain measurements and curvature analysis. Typically structural monitoring relies upon detecting structural changes through frequency and acceleration based analysis. However, curvature and strain based analysis may be a more reliable means for structural monitoring as they show more sensitivity to damage compared to modal parameters such as displacement mode shapes and natural frequency. Additionally, long gage FBG strain sensors offer a promising alternative to traditional dynamic measurement methods as the curvature can be computed directly from the FBG strain measurements without the need for numerical differentiation. Small scale experimental testing was performed using an aluminum beam instrumented with a series of FBG optical fiber sensors. Dynamic strain measurements were obtained as the aluminum beam was subjected to various loading and support conditions. From this, a novel normalized parameter based on the curvature from the dynamic strain measurements has been identified as a potential damage sensitive feature. Theoretical predictions and experimental data were compared and conclusions carried out. The results demonstrated the potential of the novel normalized parameter to facilitate dynamic monitoring at both the local and global scale, thus allowing assessment of the structures health.
KW - Structural health monitoring
KW - curvature
KW - damage sensitive feature
KW - dynamic strain measurements
KW - long-gage fiber optic sensors
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U2 - 10.1117/12.2219323
DO - 10.1117/12.2219323
M3 - Conference contribution
AN - SCOPUS:84978646737
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Health Monitoring of Structural and Biological Systems 2016
A2 - Kundu, Tribikram
PB - SPIE
T2 - Health Monitoring of Structural and Biological Systems 2016
Y2 - 21 March 2016 through 24 March 2016
ER -