Enhancing Microwave Photonic Interrogation Accuracy for Fiber-Optic Temperature Sensors via Artificial Neural Network Integration
Roman Makarov,
Mohammed R. T. M. Qaid,
Alaa N. Al Hussein,
Bulat Valeev,
Timur Agliullin,
Vladimir Anfinogentov,
Airat Sakhabutdinov
Affiliations
Roman Makarov
Department of Radiophotonics and Microwave Technologies, Kazan National Research Technical University Named after A. N. Tupolev—KAI, 10, K.Marx St., Kazan 420111, Russia
Mohammed R. T. M. Qaid
Department of Radiophotonics and Microwave Technologies, Kazan National Research Technical University Named after A. N. Tupolev—KAI, 10, K.Marx St., Kazan 420111, Russia
Alaa N. Al Hussein
Department of Radiophotonics and Microwave Technologies, Kazan National Research Technical University Named after A. N. Tupolev—KAI, 10, K.Marx St., Kazan 420111, Russia
Bulat Valeev
Department of Radiophotonics and Microwave Technologies, Kazan National Research Technical University Named after A. N. Tupolev—KAI, 10, K.Marx St., Kazan 420111, Russia
Timur Agliullin
Department of Radiophotonics and Microwave Technologies, Kazan National Research Technical University Named after A. N. Tupolev—KAI, 10, K.Marx St., Kazan 420111, Russia
Vladimir Anfinogentov
Department of Radiophotonics and Microwave Technologies, Kazan National Research Technical University Named after A. N. Tupolev—KAI, 10, K.Marx St., Kazan 420111, Russia
Airat Sakhabutdinov
Department of Radiophotonics and Microwave Technologies, Kazan National Research Technical University Named after A. N. Tupolev—KAI, 10, K.Marx St., Kazan 420111, Russia
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.
