Defect detection method for metal tubes through electromagnetic wave propagation characteristics analysis

被引:0
|
作者
Tsurubuchi K. [1 ]
Hiroki S. [1 ]
Haga N. [1 ]
Motojima K. [1 ]
机构
[1] Graduate School of Engineering, Gunma University, 1-5-1, Tenjincho, Kiryu, Gunma
来源
IEEJ Trans. Ind Appl. | / 10卷 / 786-790期
关键词
Defect detection; Electromagnetic wave propagation; Metallic tube; Non destructive inspection (NDI);
D O I
10.1541/ieejias.137.786
中图分类号
学科分类号
摘要
Metallic tubes used in power plants and industrial plants may develop cracks and change in shape owing to external factors and long-term usage. To prevent accidents caused by such defects in the metallic tubes, various non destructive inspection (NDI) methods have been established. However, these conventional methods require a considerable amount of detection time and effort to inspect the long tubes. To avoid this problem, we propose a method using propagation characteristics of electromagnetic waves. In the previous paper, we proposed the new NDI method by using the electromagnetic wave. However, it was necessary to use "a reflected wave from the normal metallic tube without any defect" as a reference. In this paper, we propose an original method for obtaining precision in defect detection without the requirement of "a reflected wave from the normal metallic tube without any defect". By using this original precisions method, we can realize more versatile detection methods. © 2017 The Institute of Electrical Engineers of Japan.
引用
收藏
页码:786 / 790
页数:4
相关论文
共 42 条
  • [21] Defect detection method for complex surface based on human visual characteristics and feature extracting
    Du, Yubin
    Cao, Pin
    Yang, Yongying
    Wang, Fanyi
    Liu, Rongzhi
    Wu, Fan
    Zhang, Pengfei
    Chai, Huiting
    Jiang, Jiabin
    Zhang, Yihui
    Feng, Guohua
    Xiao, Xiang
    Li, Yanwei
    TENTH INTERNATIONAL SYMPOSIUM ON PRECISION ENGINEERING MEASUREMENTS AND INSTRUMENTATION, 2019, 11053
  • [22] IMAGE PROCESSING WITH DEEP LEARNING: SURFACE DEFECT DETECTION OF METAL GEARS THROUGH DEEP LEARNING
    Balcioglu, Yavuz Selim
    Sezen, Bulent
    Gok, M. Sahin
    Tunca, Sezai
    MATERIALS EVALUATION, 2022, 80 (02) : 44 - 53
  • [23] FDTD Analysis of Electromagnetic Wave Propagation in an Inhomogeneous Ionosphere under Arbitrary-Direction Geomagnetic Field
    Kweon, Jun-Ho
    Park, Min-Seok
    Cho, Jeahoon
    Jung, Kyung-Young
    JOURNAL OF ELECTROMAGNETIC ENGINEERING AND SCIENCE, 2018, 18 (03): : 212 - 214
  • [24] An Optimization Method for PCB Surface Defect Detection Model Based on Measurement of Defect Characteristics and Backbone Network Feature Information
    Liu, Huixiang
    Zhao, Xin
    Liu, Qiong
    Chen, Wenbai
    SENSORS, 2024, 24 (22)
  • [25] A Novel Ultrasonic Guided Wave-Based Method for Railway Contact Wire Defect Detection
    Chang, Yong
    Li, Nana
    Zhao, Jiyuan
    Wang, Yu
    Yang, Zhe
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 71
  • [26] Defect detection using a new ultrasonic guided wave modal analysis technique (UMAT)
    Yan, Fei
    Rose, Joseph L.
    HEALTH MONITORING OF STRUCTURAL AND BIOLOGICAL SYSTEMS 2010, PTS 1 AND 2, 2010, 7650
  • [27] The Meshless Local Petrov-Galerkin Method in Two-Dimensional Electromagnetic Wave Analysis
    Nicomedes, Williams L.
    Mesquita, Renato Cardoso
    Moreira, Fernando Jose da Silva
    IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 2012, 60 (04) : 1957 - 1968
  • [28] Simulation on Propagation Characteristics of Ultra-High Frequency (UHF) Electromagnetic Wave Inside Typical Gas Insulated Switchgear (GIS) Structures
    Li Bo
    Gong Li
    Cao Min
    Li Shilin
    Lin Zhongai
    Lin Cong
    Wang Xianpei
    JOURNAL OF NANOELECTRONICS AND OPTOELECTRONICS, 2019, 14 (04) : 551 - 562
  • [29] An efficient defect detection method for nuclear-fuel rod grooves through weakly supervised learning
    Li, Mengyuan
    Chen, Ning
    Suo, Xinyu
    Yin, Shaohui
    Liu, Jian
    MEASUREMENT, 2023, 222
  • [30] Subsurface Defect Detection Method of Optical Elements Based on Through-Focus Scanning Optical Microscopy
    Wang, Na
    Liu Lituo
    Song Xiaojiao
    Wang Dezhao
    Wang Shengyang
    Li Guannan
    Zhou Weihu
    ACTA OPTICA SINICA, 2023, 43 (21)