• Title/Summary/Keyword: non-destructive testing expert system

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Development of an intelligent and integrated system for automatic inspection of steam-generator tubes in nuclear power plant (원전 증기 발생기 전열관 검사 자동화를 위한 지능형 통합 시스템 개발)

  • Kang, Soon-Ju;Choi, Yoo-Rark;Choe, Seong-Su;Woo, Hee-Gon
    • Journal of Institute of Control, Robotics and Systems
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    • v.2 no.3
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    • pp.236-241
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    • 1996
  • This paper presents a new eddy current testing system for inspecting tubes of steam generator in nuclear power plant. The proposed system adopted embedded expert system concept to automate tasks of the inspection such as inspection planning and flaw signal interpretation, and integrated all the tasks into a client/server type computing architecture using database management system. Therefore, human factor errors occurred during inspection could be minimized and the inspection data could be transferred in real-time. As a result, we can increase the level of inspection confidence and the productivity of a personal inspector. A prototype of the proposed system has been developed for 5 years and the test operation has been performed in domestic nuclear power plants.

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An Image Processing Algorithm for a Visual Weld Defects Detection on Weld Joint in Steel Structure (강구조물 용접이음부 외부결함의 자동검출 알고리즘)

  • Seo, Won Chan;Lee, Dong Uk
    • Journal of Korean Society of Steel Construction
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    • v.11 no.1 s.38
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    • pp.1-11
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    • 1999
  • The aim of this study is to construct a machine vision monitoring system for an automatic visual inspection of weld joint in steel structure. An image processing algorithm for a visual weld defects detection on weld bead is developed using the intensity image. An optic system for getting four intensity images was set as a fixed camera position and four different illumination directions. The input images were thresholded and segmented after a suitable preprocessing and the features of each region were defined and calculated. The features were used in the detection and the classification of the visual weld defects. It is confirmed that the developed algorithm can detect weld defects that could not be detected by previously developed techniques. The recognized results were evaluated and compared to expert inspectors' results.

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