Deep Learning Assisted Pressure Sensing Based on Sagnac Interferometry Realized by Side-Hole Fiber

In this paper, a novel deep learning characterization approach is proposed and demonstrated experimentally using a pressure sensor based on a Sagnac interferometer realized by a side-hole fiber. To enlarge the measurement range limited by the free spectral range, a long short-term memory (LSTM) neural network was proposed. The original spectra were recorded by a low-cost spectrometer, and the intensity of scaled spectra was used to construct one-dimension (1D) spectral data. The results show that the coefficient of determination (R²) for the pressure prediction can reach 0.9996148 with the root mean square error (RMSE) equal to 2.559 × 10⁻² MPa. Moreover, two-dimension (2D) data were obtained with the ascension algorithm of the Gramian angle field (GAF). A better R² of 0.9999908 and a lower RMSE of 4.365×10⁻³ MPa can be obtained since the ascension algorithm could retrieve deeper features among the spectral data. The proposed approach can be adopted for a similar sensor structure, showing great potential in sensing applications requiring low cost and robustness.