A Temperature Self-Compensating Fiber-Optic Fabry–Perot Sensor for High-Sensitive Vector Magnetic Field Measurement

Abstract

A parallel Fabry-Perot interferometers (FPIs) fiber-optic sensor based on magnetic fluid (MF) and Vernier effect is proposed. The proposed sensor consists of two parallel FPIs, fabricated by splicing a section of hole-assisted one-core fiber (HAOCF) onto an Au-plated single-mode fiber (APSMF). The side holes of the HAOCF in two FPIs are filled with MF and polydimethylsiloxane, respectively. This configuration allows the sensor to utilize the Vernier effect to enhance magnetic field detection sensitivity while achieving temperature self-compensation within the operating range. Additionally, the asymmetric structure of the sensor produces different spectral responses to varying magnetic field directions. The spectral data under different magnetic field directions are collected and used to train the designed convolutional neural network (CNN). Combined with the trained CNN, the sensor overcomes the limitations of traditional wavelength demodulation methods and realizes the accurate identification of magnetic field direction in the range of 0°–360°. Experimental results show that the magnetic field sensitivity of the sensor reaches −1.27 nm/mT within the 0–7-mT range, which is 5.52 times higher than that of FPI1. The temperature crosstalk of the sensor is only $5.08\times 10^{-3}$ mT/°C, a reduction of 15.87 times compared to FPI1. The sensor achieved a prediction error of less than 0.37° for the magnetic field direction on the testing dataset. This work offers a novel methodology for optical fiber sensing in vector magnetic field detection applications.