A novel roundness error evaluation method for high-speed EMU train axles

Abstract

This article proposes a novel roundness error evaluation method for high-speed electric multiple units (EMU) train axles to evaluate the roundness error of the minimum zone circle. This method utilizes the adaptive ability of Bayesian linear regression to the data to solve the initial center of the circle, avoiding the multiple search process, which not only improves the data utilization and simplifies the solving steps but also has a strong noise immunity. The method first establishes a Hough space model, maps the collected data points to the Hough space, and forms different conical surfaces; then, by Bayesian linear regression, the probability density function is set with the distance from the space point to the conical surface as a parameter, and through continuous iteration, the point with the highest probability value is the more accurate initial center of the circle. Finally, the final optimal center of the circle is obtained by the principle of minimum inclusion region. We conducted experimental studies on axles of different diameters and compared them with other methods. The results prove the practicality and effectiveness of the technique.