A New Method of Angle Measurement Error Analysis of Rotary Encoders

Hua-Kun Jia,Yi-Zhou Jiang,Lian-Dong Yu,Hui-Ning Zhao

doi:10.3390/app9163415

Abstract

In this article, a method of error source analysis and detection to improve the angle measurement accuracy of rotary encoders in precision measuring instruments is proposed. The angle measurement error caused by the installation eccentricity of the grating disk and the radial error motion of the rotating shaft is analyzed, and the error model is built. The method of measuring the radial error motion is introduced, and the visual system and image processing technology is proposed to detect the eccentricity. The verification experiment by the use of an autocollimator and a polygon is carried out. The residual error after comparison within ±6″ accounts for 9% of the angle measurement error. The proposed error model is verified, and the angle measurement error can be predicted if the installation eccentricity and radial error motion are known.

Highlights

The precision measuring instruments that contain rotation joints and high-accuracy angle sensors, such as laser trackers, articulated arm coordinate measuring machines, total stations, and theodolites, have been widely used in the industrial fields of large-scale metrology [1,2]
The angle measurement error can be predicted if the installation eccentricity and radial error motion are known
The angle measurement error caused by the installation eccentricity and radial error motion δY(θ) are known

Summary

Introduction

The precision measuring instruments that contain rotation joints and high-accuracy angle sensors, such as laser trackers, articulated arm coordinate measuring machines, total stations, and theodolites, have been widely used in the industrial fields of large-scale metrology [1,2]. Rotary encoders are widely used as high-accuracy angle sensors. Zheng et al proposed an eccentricity error model, and they used a polygon and an autocollimator to detect the angle measurement error. The grating disk eccentricity error model parameters were estimated using the nonlinear least square method. Gao et al analyzed the angle measurement error data with the fast Fourier transform (FFT), and the constant was calculated based on particle swarm optimization (PSO) to overcome the non-convergence of the least square method [4]

Methods

Results

Discussion

Conclusion