Ultra-high-temperature sensing using fiber grating sensor and demodulation method based on support vector regression optimized by a genetic algorithm

We propose an ultra-high-temperature sensing method using a fiber Bragg grating (FBG) and demodulation technique based on support vector regression optimized by a genetic algorithm (GA-SVR). A type-I FBG inscribed by a femtosecond laser in a silica fiber was packaged with a tube and used as a temperature sensor. The external ambient temperature was retrieved from the transient FBG wavelength and its increase rate in reaching thermal equilibrium of the FBG with the external environment using GA-SVR. The temperature sensing in the range of 400 to 1000 °C was realized with an accuracy of 4.8 °C. The highest sensing temperature exceeded the FBG resisting temperature of 700 °C. The demodulation time was decreased to approximately 15 s, only 3.14% of the FBG sensor time constant. The proposed method could realize the external ambient temperature determination before the FBG temperature reached the thermal equilibrium state, which enables to attain a demodulation time shorter than the time constant of the FBG sensor and a sensing temperature higher than the FBG resisting temperature. This method could be potentially applied in temperature inspection of combustion and other fields.