Enhanced Fiber Bragg Grating Interrogation Using Deep Learning and Fabry-Pérot Liquid Crystal: A CGAN-CNN for Improved Wavelength Detection

In this article, we propose a novel method that integrates deep learning with Fabry-Pérot liquid crystal (FP-LC) technology for fiber Bragg grating (FBG) interrogation. The use of FP-LC enhances the measurement range and enables high sensitivity in FBG sensors, making them appropriate for a wide range of applications requiring precise and responsive sensing. However, collecting a large amount of real experimental FBG sensor data is time-consuming, technically challenging, and resource-intensive. To address this issue, we utilize a conditional generative adversarial network (CGAN) to generate a sufficient amount of synthetic training data. The CGAN generates data conditioned on real FBG sensor data, ensuring that the generated data closely look like real experimental data distributions, which is crucial for effective model training. Moreover, we proposed a convolutional neural network (CNN) method to solve crosstalk problems, to improve sensing accuracy, and to precisely detect the peak wavelength of each FBG sensor. The experimental results demonstrated that the proposed CGAN technique effectively generates a large amount of data to improve the performance of the proposed CNN model. Furthermore, the results proved that the CNN trained on CGAN-generated data significantly improves the detection speed and accuracy of central wavelength measurements compared to traditional approaches. Hence, the proposed system is cost-effective, easy to set up for experiments, increases the feasibility and portability of modularization, fast and flexible, overcoming data shortages, and improving the sensing accuracy of wavelength detection for FBG sensor systems.