Liu Di, Shi Zhen, Yang Ziyi, Zou Chenxi
School of Geology Engineering and Geomatics, Chang'an University, 126 Yanta Road, Xi'an 710054, China.
Sensors (Basel). 2024 Dec 15;24(24):8005. doi: 10.3390/s24248005.
To eliminate the noise interference caused by continuous external environmental disturbances on the rotor signals of a maglev gyroscope, this study proposes a noise reduction method that integrates an adaptive particle swarm optimization variational modal decomposition algorithm with a strategy for error compensation of the trend term in reconstructed signals, significantly improving the azimuth measurement accuracy of the gyroscope torque sensor. The optimal parameters for the variational modal decomposition algorithm were determined using the adaptive particle swarm optimization algorithm, allowing for the accurate decomposition of noisy rotor signals. Additionally, using multi-scale permutation entropy as a criterion for discriminant, the signal components were filtered and summed to obtain the denoised reconstructed signal. Furthermore, an empirical mode decomposition algorithm was employed to extract the trend term of the reconstructed signal, which was then used to compensate for the errors in the reconstructed signal, achieving significant noise reduction. On-site experiments were conducted on the high-precision GNSS baseline of the Xianyang Yuan Tunnel in the second phase of the project to divert water from the Han River to the Wei River, where this method was applied to process and analyze multiple sets of rotor signals. The experimental results show that this method effectively suppresses continuous external environmental interference, reducing the average standard deviation of the compensated signals by 46.10% and the average measurement error of the north azimuth by 45.63%. Its noise reduction performance surpasses that of the other four algorithms.
为消除连续外部环境干扰对磁悬浮陀螺仪转子信号造成的噪声干扰，本研究提出一种降噪方法，该方法将自适应粒子群优化变分模态分解算法与重构信号趋势项误差补偿策略相结合，显著提高了陀螺仪扭矩传感器的方位测量精度。利用自适应粒子群优化算法确定变分模态分解算法的最优参数，实现对含噪转子信号的准确分解。此外，以多尺度排列熵作为判别准则，对信号分量进行滤波求和，得到去噪后的重构信号。进一步采用经验模态分解算法提取重构信号的趋势项，用于补偿重构信号中的误差，实现显著降噪。在引汉济渭二期工程咸阳塬隧洞高精度GNSS基线上进行现场实验，应用该方法对多组转子信号进行处理分析。实验结果表明，该方法有效抑制了连续外部环境干扰，补偿后信号的平均标准差降低了46.10%，北向方位平均测量误差降低了45.63%。其降噪性能优于其他四种算法。
