A 2-D Fiber Bragg Grating Acceleration Sensor Based on Circular Flexure Hinges Structure

This work proposes and experimentally validates a 2-D low- and medium-frequency fiber Bragg grating (FBG) acceleration sensor. The investigation of the vibration sensor uses a symmetrical circular flexure hinge structure with similar sensitivity in both measuring directions. The two FBGs are arranged differently to avoid a temperature impact; the sensitivity is double that of only one FBG fiber. First, the operating theory and theoretical system of the FBG accelerometer were analyzed. Second, the theoretical research and optimization improved the FBG sensor's design, resonance frequency, and sensitivity. Third, the viability of the optimization findings and the simulation was evaluated using ANSYS software. Finally, the product was created, and the sensor calibration was performed using the simulation results. The theoretical values and experimental findings largely concur. According to performance tests, the resonance frequencies in the x- and y- directions are 440 and 500 Hz, respectively, with a flat frequency range from 20 to 300 Hz. Furthermore, the sensor shows average sensitivities along the two spatial dimensions of 124 and 83 pm/G, respectively. The overall antiinterference between the two measuring directions is less than 6%. This sensor is useful in various applications, including vibration monitoring, machine condition monitoring, and robotics.

Automated summary

This study proposes and validates a 2-D low- and medium-frequency fiber Bragg grating (FBG) acceleration sensor. The sensor utilizes a symmetrical circular flexure hinge structure to achieve similar sensitivity in both measuring directions. The arrangement of two FBGs effectively avoids temperature impact and doubles the sensitivity. The sensor's design, resonance frequency, and sensitivity were improved through theoretical research and optimization, which were then validated through simulation and calibration. The sensor is capable of vibration monitoring, machine condition monitoring, and robotics applications.