Fiber Bragg Grating

Abstract

Fiber optics has emerged as a promising sensing technology over the past two decades. Fiber optic sensors (FOS) have generated great interest due to their small size, immunity to electro-magnetic interference and radio frequency (RF) interference, inherent safety, and accuracy. A sensor based on intensity modulation of light propagating in an optical fiber was first reported in the early 1980’s (Giallorenzi et al., 1982). Soon after, sensors based on phase modulation were also reported (Mellberg, 1983). However, both the intensity-modulated and the phase-modulated FOSs are susceptible to changes in the light intensity received by the detector. The change in light intensity could be a result of fiber loss due to scattering, absorption or bending, losses at fiber couplers or source intensity fluctuations. The intensity losses in the fiber caused by external force variations often require time averaging to assess and map the spatial changes in the loss or scattering coefficients along the fiber. As a result, the induced noise of the received signal increases, thus complicating the signal processing of the system. On the other hand, a FOS using fiber Bragg grating (FBG) provides an absolute wavelength measurement that is independent of the overall system light levels and dependent only upon the strain and temperature effects acting on the sensor. In this chapter, FBG sensor technology, which provides an absolute measurement, is described. This technique is wavelength encoded to avoid problems with scale resetting and signal intensity variation that plague the intensity and phase modulated sensors. FBG is a wavelength modulated element, making the signal immune to losses. This chapter presents the fundamental concept of FBG and dwells upon FBG-based pressure/ strain and shear force sensors.KeywordsFiber Bragg GratingFiber Bragg Grating SensorPhase MaskBragg WavelengthNeutral LayerThese 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.