Research on Demodulation System and Algorithm of Distributed Fiber Optic Sensor Networks

Demodulation methods are fundamental in the creation of high-performance interferometric fiber-optic temperature sensors. Crafting high-performance interferometric fiber-optic temperature sensors relies heavily on demonstration techniques. Nevertheless, the traditional passive 3 x 3 coupler demodulation approach fails to address several important points. As an example, the three interference signals have an imperfect phase difference due to the coupler's non-1:1:1 splitting ratio. Interferometric temperature sensors' output is severely affected by these variables. The aim of this research is to introduce a new arc-tangent technique that combines a 3 × 3 coupler with a demodulation approach that combines Bi-GRU and CNN. A nonlinear mapping is formed among the phase signal and temperature in the Bi-GRU that uses the arc-tangent technique to increase the input phase signal of the CNN network model. Using this method, the spectral ratio and phase difference in the 3 × 3 coupler do not impact temperature demodulation. The newly-made Bi-GRU method achieves high-resolution readings with an interval of 0.10 °C between 25.00 and 25.50 °C, with an absolute error of less than 0.0040 °C. After setting the temperature control to 25.00 degrees Celsius, we measured for nearly three hours to prove that the proposed method is stable and adaptable under long-term conditions of constant temperature. The experimental results show that compared to the conventional algorithm's 0.0095 °C maximum error, the Bi-GRU with CNN method achieves a significantly lower maximum error of around 0.0040 °C. In addition, the Bi-GRU with CNN approach clearly shows the most stable demodulation results when compared to the standard passive 3 × 3 coupler technique (0.0023 °C), the Bi-GRU model (0.0019 °C), and the standard passive 3 × 3 coupler technique (0.0014 °C). Thus, temperature-sensing systems built on interferometric fiber-optic technology greatly benefit from the Bi-GRU with CNN method in terms of accuracy and dependability.