Quantitative evaluation of pipe wall thinning defect sizes using microwave NDT

Yijun Guo, Guanren Chen, Takuya Katagiri, Noritaka Yusa, Hidetoshi Hashizume

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


This study investigated the applicability of microwave non-destructive testing, which has been proved effective in quickly detecting the defect location in a long pipe, to the size evaluation of wall thinning defects. Artificial wall thinning defects with different sizes (depths and lengths) and edge profiles were introduced to a flanged brass pipe with a total length of 15 m, and reflected microwave signals were measured in experiments. A signal processing method combining windowing and dispersion compensation was proposed to extract the defect-related reflection signals in the frequency domain. Resonant frequencies, at which the amplitude of extracted signals dropped significantly, decreased with the increase of either wall thinning depth or length. In addition, the results demonstrate that wall thinning location and pipe end conditions have little influence on resonant frequencies after signal processing. A back propagation neural network was trained by simulation data, using resonant frequencies as the input, to simultaneously evaluate defect depth and length, and the performance was validated by experiments. Maximum prediction errors of depth and length of wall thinning were 0.06 mm and 0.57 mm, respectively, which indicated the feasibility of the proposed method to evaluate the wall thinning defect sizes.

Original languageEnglish
Pages (from-to)737-753
Number of pages17
JournalNondestructive Testing and Evaluation
Issue number6
Publication statusPublished - 2022


  • back propagation neural network
  • microwaves
  • resonant frequency
  • signal processing

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering
  • Physics and Astronomy(all)


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