Enhancing Microwave Photonic Interrogation Accuracy for Fiber-Optic Temperature Sensors Via Artificial Neural Network Integration
Optics(2024)
摘要
In this paper, an application of an artificial neural network algorithm is proposed to enhance the accuracy of temperature measurement using a fiber-optic sensor based on a Fabry–Perot interferometer (FPI). It is assumed that the interrogation of the FPI is carried out using an optical comb generator realizing a microwave photonic approach. Firstly, modelling of the reflection spectrum of a Fabry–Perot interferometer is implemented. Secondly, probing of the obtained spectrum using a comb-generator model is performed. The resulting electrical signal of the photodetector is processed and is used to create a sample for artificial neural network training aimed at temperature detection. It is demonstrated that the artificial neural network implementation can predict temperature variations with an accuracy equal to 0.018 °C in the range from −10 to +10 °C and 0.147 in the range from −15 to +15 °C.
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关键词
fiber-optic sensors,microwave photonics,convolutional neural networks,fuzzy logic algorithms,optical frequency comb,Fabry–Perot interferometer
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