Breaking Demodulation Limitations: AWG and Deep Learning in Dynamic Spectral Reconstruction for Differential FBG Accelerometers

The conventional fiber Bragg grating (FBG) accelerometer demodulation often suffers from high-environmental sensitivity, complexity, and cost. To address these issues, this article presents two arrayed waveguide grating (AWG)-based dynamic spectral reconstruction models. First, a novel differential FBG accelerometer with an equal-intensity triangular cantilever beam and a symmetric string-like design enhances sensitivity and mitigates temperature interference. A power ratio-nonlinear logarithmic regression (PR-NLR) model derived from static calibration then enables spectral reconstruction and wavelength demodulation across varying accelerations, achieving 4-82-pm accuracy, slightly below that of high-speed demodulators (HSDs). Next, by integrating convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), attention mechanisms, and a generative adversarial network (GAN) for data augmentation, prediction errors drop to 1-26 pm at 20 Hz and 0.2-21 pm at 40 Hz-a reduction of at least 60% over PR-NLR. This outcome underscores the advantages of deep learning (DL) in managing complex spectral shifts. By overcoming frequency-only demodulation limits, the proposed AWG approach achieves precise wavelength demodulation through dynamic spectral reconstruction. Its compact design, low manufacturing cost, and robust performance facilitate scalable deployment in structural health monitoring, particularly for bridge vibration analysis and industrial machinery diagnostics.