基于单测头误差相位偏移法的嵌入式角位移传感器自校准研究

Self-calibration study of the embedded angular displacement sensor based on single probe error phase shift method

针对嵌入式角位移传感器长期使用出现精度损失问题,结合传感器结构特点提出一种基于单测头误差相位偏移法的自校准方法。首先,依据圆周空间封闭性原则,基于傅里叶变换分析传感器误差特性;然后,以起始测量值序列作为误差偏移起始基准,根据误差特性等间隔变换误差序列并解算误差与起始基准的函数关系,构建传感器误差模型;最后,搭建在线校准实验平台进行验证实验。实验结果表明,单个对极内误差结果与测量误差特性一致,整周测量误差大幅度降低,误差峰峰值由127.80″降低至5.90″;该方法校准效率相比于比较校准法提升了80.69%,校准后误差分布集中,同时具有良好的测量稳定性。本文所述自校准方法在不依赖高精度基准器具前提下,有效解决了传感器精度损失问题,对实现嵌入式角位移传感器自校准具有重要的应用意义。

To address the problem of accuracy loss of embedded angular displacement sensor after long time use, a self-calibration method using single probe error phase shift method is proposed in combination with the structural characteristics of the sensor. Firstly, the sensor error characteristics are analyzed based on the Fourier transform according to the principle of circumferential spatial closure. Secondly, the sensor error model is constructed by transforming the error sequence and solving the function relationship between the error and the starting reference with the sequence of starting measurement values as the starting reference for error offset. Finally, the online calibration experiment platform is established for verification experiments. Experimental results show that the single intra-pole error results are consistent with the measurement error characteristics, and the whole cycle measurement error is reduced significantly, with the error peak value reduced from 127.80″ to 5.90″. Comparison experiments and repeatability experiments show that the calibration efficiency of the method is improved by 80.69% compared with the comparative calibration method, and the error distribution is concentrated after calibration, while remains good measurement stability. The self-calibration method described in this article effectively solves the problem of sensor accuracy loss without relying on a high-precision reference apparatus, and has important application significance for realizing the self-calibration of embedded angular displacement sensors.