Document Type

Conference Proceeding




Faculty of Computing, Health and Science


School of Engineering / Centre for Communications Engineering Research




This is an Author's Accepted Manuscript of: Oswald, D. P., Richardson, S. J., & Wild, G. (2011). Numerical Modelling of Interrogation Systems for Optical Fibre Bragg Grating Sensors. Paper presented at the SPIE Smart Nano-Micro Materials and Devices. Melbourne, Australia. Proc. SPIE 8204. 82040Q. Available here


There are a number of interrogation methods that can be used in optical Fibre Bragg Grating (FBG) sensing system. For very high frequency signals interrogating the sensor signal from an FBG is limited to two intensiometric methods, edge filter detection and power detection. In edge filter detection, a broadband light source illuminates an FBG, the reflected spectrum is then passed through a spectral filter. In power detection, a narrowband light source with a wavelength corresponding to the 3dB point of the FBG is filtered by the FBG itself. Both methods convert the spectral shift of the FBG into intensity signals. These two categories each have a number of variations, all with different performance characteristics. In this work we present a numerical model for all of these interrogation systems. The numerical model is based on previous analytical modelling, which could only be utilised for perfect Gaussian profiles. However, interrogation systems can make use of non Gaussian shaped filters, or sources. The numerical modelling enables the different variations to be compared using identical component performance, showing the relative strengths and weakness of the systems in terms of useful parameters, including, signal-to-noise ratio, sensitivity, and dynamic resolution. The two different detection methods can also be compared side-by-side using the same FBG. Since the model is numerical, it enables real spectral data to be used for the various components (FBG, light source, filters). This has the added advantage of increasing the accuracy and usefulness of the model, over previous analytical work.



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