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We present our research on the development of a Brillouin optical frequency-domain analysis (BOFDA) using a perfluorinated graded-index polymer optical fiber (PFGI-POF) as a sensing fiber. The described works include investigations both on the selection of the setup components with respect to mode coupling effects and on the impact of the humidity cross-sensitivity. The suitability of the developed POF-based BOFDA is proved by a distributed measurement on an 86 m long PFGI-POF recorded with the spatial resolution of 6 m.
We present our achievements in the development of distributed fiber optic sensing systems based on Brillouin optical frequency-domain analysis for structural health monitoring. The focus of the applications is on the gapless monitoring of geotechnical structures, large area infrastructures and electrical grids. The work includes the latest use of machine learning algorithms to reduce measurement time by coexistent increasing the measurement accuracy.
Brillouin Optical Frequency Domain Analysis (BOFDA) is a powerful and well-established method for static distributed sensing of temperature and strain. Recently, we demonstrated a BOFDA system based on convolutional neural network which shortens the measurement time considerably. In this paper, we apply leave-one-out cross validation to evaluate the generalization performance and provide an unbiased and reliable machine learning model for a time-efficient BOFDA system.