文章摘要
杨加东,谢明,王丽华,鲍刚.基于BP神经网络的表面缺陷检测分类[J].机床与液压,2017,45(16):160-164
基于BP神经网络的表面缺陷检测分类
Surface Defect Detection and Classification Based on BP Neural Network
  
DOI:10.3969/j.issn.1001-3881.2017.16.041
中文关键词: 精密轴承  表面缺陷  图像处理  BP神经网络
英文关键词: Precision bearing  Surface defects  Image processing  BP neural network
基金项目:
作者单位E-mail
杨加东 南京工业大学电气工程与控制科学学院 yangjiadong147@163.com 
谢明 南京工业大学电气工程与控制科学学院  
王丽华 南京工业大学电气工程与控制科学学院  
鲍刚 南京工业大学电气工程与控制科学学院  
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中文摘要:
      精密轴承应用广泛,精度要求高,轴承表面缺陷对其使用影响很大。因此,对轴承缺陷的检测很有必要。目前的检测以人工为主,但当缺陷小于0.075 mm时人眼就很难识别。以CCD摄像机为视觉结合图像处理技术,设计一种轴承在线检测方法,能够在很大程度上提高检测效率和检测精度,最后利用BP神经网络进行缺陷分类,实验结果表明:分类正确率可达92.7%,符合工业要求。
英文摘要:
      Precision bearings are widely used and high requirements are required for their accuracy. The defect of bearings surface necessary to detect the bearings defect. At present, the defect detection of the bearings mainly depends on people, however, when the defect is less than 0.075 mm, it is difficult for people to identify. The charge coupled device (CCD) camera and image processing techniques were adopted to design a detection method on line, which could greatly improve the detection efficiency and accuracy. Finally, the BP neural network was used to classify the defects.The experimental results show that the classification accuracy is up to 92.7%, which meets the requirements of the industry.
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