Abstract

This article proposes a novel integration of bidirectional free-space optics (FSO) and fiber-based fiber Bragg gratings (FBG) sensor structure for a long-distance sensing signal transmission system. The proposed FSO transmission technique is crucial to address concerns of topographical limitations that make fiber cable installation complex. According to experiments, as compared the proposed bidirectional scheme with a unidirectional FSO transmission channel achieved higher output power and clear reflected spectra of FBGS after 50 kilometers of SMF signal transmission. Besides, a combination of the neural network (NN) profile, recurrent generative adversarial network (R-GAN), and gated recurrent units (GRU) methods are proposed to address the overlap spectra problem and enhance peak wavelength detection accuracy in the FBG sensor system. The two NN profile and R-GAN methods are used to collect large amounts of strain sensor data, while the GRU method is used for peak wavelength detection in the FBG sensor system. The results demonstrated that the proposed two steps of the NN and R-GAN method are capable to generate a significant amount of realistic spectral training sets to improve the performance of the GRU deep learning model. Moreover, the results indicate that the well-trained GRU model using datasets generated by the NN and R-GAN method achieves superior performance than other previously reported machine learning techniques.

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