Temperature prediction based on long short‐term memory convolutional neural network Bragg grating sensing

Abstract To address the constraints associated with conventional fitting techniques for temperature demodulation in the context of subway tunnel fires, a new method of demodulation grating sensing spectrum using long short‐term memory convolutional neural network (LSTM‐CNN) is proposed in this paper. Build the monitoring platform based on LSTM‐CNN ultra‐weak fiber grating temperature measurement system, predict its sensing signals by LSTM‐CNN algorithm, select 18000 spectra as sample data for training, use AdamW stochastic optimization algorithm for training, and carry out the temperature calibration and demodulation error analysis of the Fiber Bragg Grating within the temperature range of 25–75°C. Compared with GRU algorithm, LSTM algorithm and traditional maximum peak method, the algorithm of this paper is good and can effectively improve the measurement accuracy, the experimental results show that: the demodulation accuracy of temperature wavelength prediction in this paper can be up to 99.27%, and the root mean square deviation is 0.08528°C, through the experiments, it is verified that the method proposed in this paper has a certain reference and support in terms of theories and technology significance. It is suitable for the identification and monitoring of fire hazards in underground tunnels, and also has application value in the signal processing of grating array sensing demodulation system.