FBG Temperature Demodulation via Two-Branch Neural Network With Multi-Information Fusion

Fiber Bragg Grating (FBG) sensors are widely used for temperature monitoring in various applications through the demodulation of resonant wavelengths. Real-time demodulation of local high-temperature variations is crucial for structural health monitoring, aero-engines, and related fields. However, local high-temperature characteristics of FBGs, such as resonance peak splitting and bandwidth increase, pose challenges to precise wavelength demodulation. To address these issues, this article proposes a novel wavelength demodulation framework based on neural networks, employing an encoder-decoder architecture to achieve low-latency and high-precision results. Specifically, the reflectance spectrum, serving as the original sensing information, is encoded into different forms and processed through two branches. One branch encodes it as a Markov transition field (MTF) and uses a pretrained ResNet to extract underlying physical information, while the other utilizes a 1-D convolutional neural network (1D-CNN) to extract wavelength properties. Finally, attention modules merge representations from the two branches, which are then decoded to accomplish wavelength demodulation. The effectiveness and superiority of our framework are validated on a real temperature monitoring system we built, achieving a minimum wavelength demodulation error and dynamic interrogation range of +/- 0.07 pm and 75 nm. These results highlight the potential of the framework for near-field temperature monitoring in complex environments.