Visual Inspection of Labyrinth Disc Surface Crack in an Aeroengine Based on Industrial Videoscope

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Visual Inspection of Labyrinth Disc Surface Crack in an Aeroengine Based on Industrial Videoscope

Article | 更新时间：2020-08-13

- Visual Inspection of Labyrinth Disc Surface Crack in an Aeroengine Based on Industrial Videoscope
- Optics and Precision Engineering Vol. 17, Issue 5, Pages: 1187-1195(2009)
- 作者机构：
  
  国防科技大学机电工程与自动化学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.4
- Received：22 October 2008，
  
  Revised：16 December 2008，
  
  Published Online：25 May 2009，
  
  Published：25 May 2009
- 稿件说明：
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孙慧贤,张玉华,罗飞路. 某型航空发动机篦齿盘表面裂纹的视觉检测方法[J]. 光学精密工程, 2009,17(5):1187-1195

Visual Inspection of Labyrinth Disc Surface Crack in an Aeroengine Based on Industrial Videoscope[J]. Optics and precision engineering, 2009, 17(5): 1187-1195.
孙慧贤,张玉华,罗飞路. 某型航空发动机篦齿盘表面裂纹的视觉检测方法[J]. 光学精密工程, 2009,17(5):1187-1195 DOI：

Visual Inspection of Labyrinth Disc Surface Crack in an Aeroengine Based on Industrial Videoscope[J]. Optics and precision engineering, 2009, 17(5): 1187-1195. DOI：

摘要

基于内窥镜的视觉检测技术在现代航空发动机故障诊断中发挥着重要的作用。本文针对某型航空发动机篦齿盘均压孔上产生的表面裂纹，采用基于工业视频内窥镜的孔探技术对其进行自动视觉检测。在对均压孔裂纹形成原因和裂纹形态进行分析的基础上，提出一种基于定位-识别模型的盘孔表面裂纹检测方法。该方法采用一种新的模糊快速Hough算法检测均压孔的圆形边界，对盘孔进行定位；然后根据裂纹分布情况确定兴趣区域，并对该区域内的图像进行二值化和细化；最后采用链码分析实现裂纹识别。在实验中，对比了模糊快速Hough变换与标准Hough变换、随机Hough变换检测盘孔边界的效果，结果表明模糊快速Hough变换可以快速准确地识别出圆形边界，对盘孔进行定位。采用上述方法对80幅均压孔图像进行裂纹检测试验，结果表明检测的准确率可达91.2%。

Abstract

Visual inspection with endoscope plays an important role in modern fault diagnosing of aeronautic engine. An inspection approach of aperture’s surface crack on an aeroengine labyrinth disc is studied based on industrial videoscope. The appearance reason of the crack and its characteristic are analyzed firstly. An automatic recognition method of surface crack is proposed based on a localization-recognition model. A novel fuzzy fast Hough transform is presented and used to detect the circular contour to locate the aperture. Then

an interesting region is defined around the aperture

in which the image is binarized by a threshold and the binary image is thinned by a morphological method. Moreover

the chain codes in the image are analyzed to recognize the crack. The results of circular contour detection by the proposed fuzzy fast Hough Transform are compared with the results by Standard Hough Transform and Randomized Hough Transform respectively. The results show that the fuzzy fast Hough transform can detect the contour quickly and accurately. The test of crack detection is carried out in 80 aperture images

and the results indicate that the detecting accuracy is 91.2%.

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