• CN:11-2187/TH
  • ISSN:0577-6686

›› 2005, Vol. 41 ›› Issue (4): 180-184.

• 论文 • 上一篇    下一篇

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航空发动机叶片实时成像自动检测技术研究

周正干;赵胜;安振刚   

  1. 北京航空航天大学机械工程及自动化学院
  • 发布日期:2005-04-15

RESEARCH ON AUTOMATIC INSPECTION TECHNIQUES OF REAL-TIME RADIOGRAPHY FOR TURBINE –BLADE

Zhou Zhenggan;Zhao Sheng;An Zhengang   

  1. School of Mechanical Engineering and Automation, Beihang University
  • Published:2005-04-15

摘要: 为了实现航空发动机叶片X射线实时成像自动检测,以基于平板探测器的X射线实时成像系统为研究对象,根据射线实时成像的特点,对航空发动机叶片缺陷的提取技术进行了研究。根据航空发动机叶片X射线数字图像灰度变化的特点,将叶片图像划分为6个区域,分别进行基于扫描线的自适应中值滤波模拟出缺陷的背景;将原始图像与背景图像相减,获得背景平坦、缺陷突出的差值图像;通过对差值图像进行阈值分割,实现对缺陷的分离;用数学形态学的开运算对缺陷图像滤波以去除噪声;为保证缺陷的准确性,用区域生长法重新生长出缺陷;最后提取出缺陷的特征参数。将缺陷提取的结果和人工评片的结果比较,证明这种方法是准确、有效的,为实现航空发动机叶片X射线自动检测打下了良好的基础。

关键词: X射线实时成像, 缺陷提取, 图像处理, 无损检测, 自适应中值滤波

Abstract: To inspect turbine blade automatically, with a realtime radiographic system based on X-ray flat panel detector, computerized defect extraction techniques are studied on the basis of characteristics of turbine blade’s digital radiographic images. At first, in the light of a variety of gray-level in a turbine blade’s digital radiographic image, it is divided into six subareas. An adaptive median filter is used to smooth defects in each subarea. Then, the filtered image is subtracted from the raw image and a difference image with flat background and outstanding defects is obtained. After that, thresholding is applied to the difference image and defects in the turbine blade became obvious. Later on, a morphological opening is used to realize noise reduction. In order to ensure the accuracy of defects, a region growing method is adopted to reconstruct the defects. Finally, the feature data of defects are extracted. The comparison between computerized feature extraction results and human interpretation results indicated that the method mentioned above is effective and efficient, which laid a good foundation for automatic inspection of turbine-blade with X-ray.

Key words: Defect extraction, Image processing, Non-destructive testing, Real-time radiography, Self-adaptive median filtering

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