1 April 2016 A curvature based approach using long-gage fiber optic sensors
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Abstract
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.
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Kaitlyn Kliewer, Kaitlyn Kliewer, Branko Glisic, Branko Glisic, "A curvature based approach using long-gage fiber optic sensors", Proc. SPIE 9805, Health Monitoring of Structural and Biological Systems 2016, 980520 (1 April 2016); doi: 10.1117/12.2219323; https://doi.org/10.1117/12.2219323
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