Modelling of dynamic measurement error for parasitic time grating sensor based on Bayesian principle

Hong-tao YANG; Liu-sha ZHANG; Jiao ZHOU; Ye-tai FEI; Dong-lin PENG

doi:10.3788/OPE.20162410.2523

您当前的位置：

首页 >

文章列表页 >

Modelling of dynamic measurement error for parasitic time grating sensor based on Bayesian principle

Information Sciences | 更新时间：2020-07-07

- Modelling of dynamic measurement error for parasitic time grating sensor based on Bayesian principle
- Optics and Precision Engineering Vol. 24, Issue 10, Pages: 2523-2531(2016)
- 作者机构：
  
  1.安徽理工大学机械工程学院, 安徽淮南 232001
  2.合肥工业大学仪器科学与光电工程学院, 安徽合肥 230009
  3.重庆理工大学电子信息与自动化学院, 重庆 400054
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20162410.2523
  CLC： TP212.12
- Received：12 June 2016，
  
  Accepted：19 July 2016，
  
  Published：2016-10
- 稿件说明：
移动端阅览
Hong-tao YANG, Liu-sha ZHANG, Jiao ZHOU, et al. Modelling of dynamic measurement error for parasitic time grating sensor based on Bayesian principle[J]. Optics and precision engineering, 2016, 24(10): 2523-2531.
DOI：

Hong-tao YANG, Liu-sha ZHANG, Jiao ZHOU, et al. Modelling of dynamic measurement error for parasitic time grating sensor based on Bayesian principle[J]. Optics and precision engineering, 2016, 24(10): 2523-2531. DOI： 10.3788/OPE.20162410.2523.

摘要

为了提高寄生式时栅传感器的测量精度，分析了它的工作原理和动态误差组成，得到其主要误差分量为常值误差、周期误差和随机误差等。针对寄生式时栅误差特点，建立了寄生式时栅动态误差高精度预测模型，并与其他建模方法进行了比较。选用插入标准值的贝叶斯预测模型，以实际测量的传感器第一个对极动态误差数据进行建模，在后续对极特定位置插入部分实际误差测量数据，建立误差预测模型，预测了传感器后83个对极的动态误差。另选用三次样条插值和BP神经网络建模方法对寄生式时栅整圈动态误差建模，并与建立的误差模型进行了对比。验证实验表明，三次样条插值建模时间最短（0.62 s），但其建模精度不高（16.050 0"）；贝叶斯动态模型建模时间（0.86 s）略长于三次样条插值，但建模精度最高（0.415 3"）；BP神经网络建模时间最长（32 min），但建模精度最低（19.680 2"）。同时贝叶斯插入标准值建模方法所需数据点（69395个）远少于三次样条和BP神经网络建模数据点（235526个），节省了大量的标定时间和建模数据量，因此可用于寄生式时栅传感器的动态测量误差高精度建模修正。

Abstract

To improve the measurement accuracy of a parasitic time grating sensor

the working principle and dynamic error composition of the sensor were analyzed deeply and the main error components including constant error

periodic error and random error were obtained. According to the error characteristics of parasitic time grating

a high precise prediction model for dynamic error of the parasitic time grating was established and the modeling method was compared with other modeling methods. The Bayesian prediction model interpolated with standard values was chosen to build the error prediction model based on the actually measured dynamic error data of first pole in the sensor. Then

a part of actual measurement error data were inserted in the specific location of subsequent pole to establish the error prediction model to predict the dynamic error of 83 poles of the sensor. The modeling method of cubic spline interpolation and BP neural network were used to build the whole circle dynamic error model of parasitic time grating sensor and compared with the above Bayesian model. The modeling verification experiment results show that the modeling time of cubic spline interpolation method is the shortest (0.62 s)

but the modeling accuracy is not high(16.050 0"). The modeling time of Bayesian prediction model is slightly longer than that of the cubic spline interpolation(0.86s)

but the modeling accuracy is the highest one(0.415 3"). The modeling time of BP neural network method is the longest one (32 min)

and the modeling accuracy is the lowest one (19.680 2"). Moreover

the modeling data points of Bayesian prediction model interpolated with standard value(69395) is far less than that of cubic spline interpolation and BP neural network(235526). Therefore

Bayesian prediction model interpolated with standard values saves a lot of calibration time and modeling data points

and can be used for high precision modeling and dynamic measurement error correction of parasitic time grating sensors.

关键词

Keywords

references

彭东林,李彦,付敏,等. 用于极端和特殊条件下机械传动误差检测的寄生式时栅研究[J]. 仪器仪表学报,2013,34(2):359-365.

PENG D L, LI Y, FU M, et al.. Study on parasitic time grating sensors used for mechanical transmission error measurement under harsh and special environment[J]. Chinese Journal of Scientific Instrument, 2013,34(2):359-365.(in Chinese)

孙世政,彭东林,郑方燕,等. 时栅传感器动态测量误差补偿[J]. 光学精密工程,2015,23(4):1114-1121.

SUN SH ZH, PENG D L, ZHENG F Y, et al.. Compensation for dynamic measurement errors of time grating sensor[J]. Opt. Precision Eng.,2015,23(4):1114-1121.(in Chinese)

龚蓬,费业泰. 动态测量误差灰色预测建模识别参数修正方法[J]. 应用科学学报,2002,20(2):207-210.

GONG P, FEI Y T. A method for correcting the identification parameters of grey prediction modeling of measurement errors[J]. Journal of Applied Sciences, 2002,20(2):207-210. (in Chinese)

高贯斌,王文,林铿,等. 基于RBF神经网络的关节转角误差补偿[J]. 机械工程学报,2010,46(12):20-24.

GAO G B, WANG W, LIN K, et al.. Error compensation of joint angles based on RBF neural networks[J]. Journal of Mechanical Engineering, 2010,46(12):20-24. (in Chinese)

GUAJARDO J, WEBER R, CRONE S F. A study on the ability of support vector regression and neural networks to forecast basic time series patterns[J]. International Federation for Information Processing Digital Library, 2010,217(1):149-157.

GUMAROV S G, KORSUN O A. A method of determining the dynamic error of optical trajectory measurement stations[J]. Measurement Technique, 2011, 54(3):281-286.

杨继森,陈锡侯,王文略,等. 时栅位移传感器高精度驱动电源研究与设计[J]. 仪表技术与传感器,2010(10):5-7.

YANG J S, CHEN X H, WANG W L, et al.. Study and design of driving power of time grating displacement sensor[J]. Instrument Technique and Sensor, 2010,(10):5-7. (in Chinese)

彭东林,刘小康,张兴红,等. 基于谐波修正法的高精度时栅位移传感器[J]. 仪器仪表学报,2006,27(1):31-33.

PENG D L, LIU X K, ZHANG X H, et al.. High-precision time-grating displacement sensor based on harmonic wave correcting method[J]. Chinese Journal of Scientific Instrument, 2006,27(1):31-33. (in Chinese)

杨洪涛,章刘沙,周姣,等. 寄生式时栅安装误差对传感器测量精度的影响[J]. 光学精密工程,2016,24(2):319-326.

YANG H T, ZHANG L SH, ZHOU J, et al.. Effect of installation error of parasitic time grating on sensor measuring accuracy[J]. Opt. Precision Eng.,2016,24(2):319-326.(in Chinese)

杨洪涛. 坐标测量机误差建模与修正技术研究[D]. 合肥:合肥工业大学,2007. http://cdmd.cnki.com.cn/article/cdmd-10359-2007102671.htm

YANG H T. Research on error model building and error correcting technique of coordinate measuring machines[D]. Hefei:Hefei University of Technology, 2007.

Views

355

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Effect of installation error of parasitic time grating on sensor measuring accuracy

Influence of frequency mixing on measurement accuracy of full-field heterodyne dynamic interferometer

Compensation for dynamic measurement errors of time grating sensor

Determination of measurement point distribution for contact measurement of large aspheric mirror surface

Error compensation and parameter identification of circular grating angle sensors

Related Author

PENG Dong-lin

FEI Ye-tai

ZHOU Jiao

ZHANG Liu-sha

YANG Hong-tao

WU Zhou

ZHANG Wenxi

LÜ Tong

Related Institution

School of Mechanical Engineering, Anhui University of Science and Technology

School of Electronic Information and Automation, Chongqing University of Technology, Chongqing 400054, China

Aerospace Information Research Institute，Chinese Academy of Sciences

School of Optoelectronics，University of Chinese Academy of Sciences

School of Instrument Science and Optoelectronic Engineering, Hefei University of Technology

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰