Dual-Parameter Demodulation of FBG-FPI Cascade Sensors via Sparse Samples: A Deep Learning-Based Perspective

This article proposes a complete framework for the manufacturing and dual-parameter demodulation of sensor systems consisting of cascaded fiber Bragg gratings (FBGs) and Fabry–Perot interferometers (FPIs). The proposed system aims to enable simultaneous interrogation of the external strain and temperature using a machine learning (ML) technique, with limited data availability and without requiring an optical spectrum analyzer (OSA). The system converts the sensing signals of a cascaded FBG-FPI sensor into changes in the transmitted optical intensity across multiple channels of the array waveguide grating (AWG). By inputting the transmitted optical intensity and wavelength shift data into a neural network, an intricate nonlinear relationship is established, which enables precise interrogation of the peak wavelength. Furthermore, to overcome the common limitation of insufficient data in data-driven models, we introduce a high-performance data augmentation method that utilizes a generative adversarial network (GAN) to rapidly augment the dataset during the training process. Extensive experiments using a real-world dataset generated in an industrial optical fiber sensing scenario demonstrate the effectiveness and superiority of the proposed framework.