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 paper 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 and uses a pre-trained ResNet to extract underlying physical information, while the other utilizes a 1D convolutional neural network 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.