欢迎访问《兵工学报》官方网站,今天是 分享到:

兵工学报 ›› 2022, Vol. 43 ›› Issue (1): 190-198.doi: 10.3969/j.issn.1000-1093.2022.01.021

• 论文 • 上一篇    下一篇

基于耦合去噪算法的航空发动机中Si3N4圆柱滚子表面缺陷的检测方法

廖达海1,2, 殷明帅1,2, 罗宏斌1, 黄佳雯2,3, 吴南星1,2   

  1. (1.景德镇陶瓷大学 机械电子工程学院, 江西 景德镇 333403; 2.江西省陶瓷材料加工技术工程实验室, 江西 景德镇 333403;3.景德镇学院, 江西 景德镇 333403)
  • 上线日期:2022-03-01
  • 作者简介:廖达海(1987—), 男, 讲师, 博士研究生。E-mail: 15172480512@163.com;
    殷明帅(1995—), 男, 硕士研究生。E-mail: apolloyms@163.com
  • 基金资助:
    国家自然科学基金项目(51964022)

Detection and Analysis of Surface Defects of Si3N4 Cylindrical Roller in Aero-engine Based on Coupled Denoising Algorithm

LIAO Dahai1,2, YIN Mingshuai1,2, LUO Hongbin1, HUANG Jiawen2,3, WU Nanxing1,2   

  1. (1.School of Mechanical and Electronic Engineering, Jingdezhen Ceramic University,Jingdezhen 333403,Jiangxi, China;2.Jiangxi Ceramic Material Processing Technology Engineering Laboratory, Jingdezhen 333403, Jiangxi, China; 3.Jingdezhen University, Jingdezhen 333403,Jiangxi, China)
  • Online:2022-03-01

摘要: 为解决基于机器视觉的传统单一图像去噪算法对混合噪声信号处理效果不佳,导致不能有效地检测识别航空发动机中应用的Si3N4圆柱滚子表面缺陷问题,提出一种基于改进的耦合去噪算法与多尺度阈值分割算法相结合的视觉检测方法。通过优化的小波阈值去噪算法与改进的中值滤波算法相耦合方法对Si3N4圆柱滚子的表面缺陷图像进行去噪处理,采用多尺度阈值分割算法对缺陷图像进行图像分割,识别提取Si3N4圆柱滚子表面缺陷。实验结果表明:Si3N4圆柱滚子表面缺陷图像经过改进的耦合去噪算法进行去噪后,信噪比>24.5%,多尺度阈值分割算法对Si3N4圆柱滚子表面缺陷图像的检测识别准确率>94%;该视觉检测方法具有良好的图像去噪效果,为进一步图像的缺陷识别打下基础,并且具有一定的通用性。

关键词: 机器视觉, Si3N4圆柱滚子, 耦合去噪, 表面缺陷, 多尺度阈值分割

Abstract: In order to solve the problem that the traditional single image denoising algorithm based on machine vision has poor effect on the mixed noise signal processing, resulting in the inability to effectively detect and identify the surface defects of Si3N4 cylindrical roller used in aero-engine, a visual detection method based improved coupled denoising algorithm and multi-scale threshold segmentation algorithm is proposed. The surface defect image of Si3N4 cylindrical roller is denoised by the optimized wavelet threshold denoising algorithm and the improved median filter algorithm, and the multi-scale threshold segmentation algorithm is used to segment the defect image, thus identifying and extracting Si3N4 surface defects of cylindrical rollers. The experimental results show that the signal-to-noise ratio of surface defect images of Si3N4 cylindrical rollers denoised by the improved coupling denoising algorithm is more than 24.5%, and the detection and recognition accuracy rate of the multi-scale threshold segmentation algorithm for surface defect images of Si3N4 cylindrical rollers is more than 94%. It proves that the visual detection method has a good image denoising effect and a certain versatility.

Key words: machinevision, Si3N4cylindricalroller, couplingdenoising, surfacedefect, multi-scalethresholdsegmentation

中图分类号: