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Distributed Fiber Optic Strain Measurement Using Rayleigh Scatter in Composite Structures

  • Eric E. SanbornEmail author
  • Alex K. Sang
  • Ed Wesson
  • Donald E. WigentIII
  • Gregory Lucier
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

This paper presents the use of distributed fiber optic sensing to achieve centimeter level resolution strain data along the entire length of a large composite beam. A 6.5 meter long composite beam, designed for use in a corrosive flue gas desulfurization (FGD) unit, was instrumented. A section of optical fiber was embedded into a fiberglass rope, which in turn was embedded into the composite beam during the manufacturing process. The beam was experimentally tested in four-point bending at the North Carolina State University Constructed Facilities Laboratory, and the strain profile along the entire length was measured using the embedded optical fiber. Strains of up to 6500 microstrain were measured at over 300 unique positions along the span by monitoring changes in the spectral shift of the Rayleigh scatter in the optical fiber using optical frequency domain reflectometry (OFDR). The fiber used in this test was optically equivalent to standard telecommunication fiber, allowing for low-cost, high-density strain measurements on large structures. The experiment confirms the potential of embedded fiber optic distributed sensing to be used for real-time health monitoring, or as a process feedback in an instrumented structural system. Benefits of employing distributed fiber optic sensing in structures such as the composite FGD unit include the ability to monitor and detect deterioration and damage, minimize the chance of unplanned downtime or failure, and limit exposure to consequences such as environmental contamination.

Keywords

Composite Beam Glass Fiber Reinforce Polymer Fiber Optic Sensor Strain Profile Rayleigh Scatter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Businees Media, LLC 2011

Authors and Affiliations

  • Eric E. Sanborn
    • 1
    Email author
  • Alex K. Sang
    • 1
  • Ed Wesson
    • 2
  • Donald E. WigentIII
    • 3
  • Gregory Lucier
    • 4
  1. 1.Luna TechnologiesBlacksburgUSA
  2. 2.Non-Metallic Resources (RPS)MobileUSA
  3. 3.3TEX Inc.CaryUSA
  4. 4.Constructed Facilities LaboratoryNorth Carolina State UniversityRaleighUSA

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