TY - JOUR
T1 - Quantitative evaluation of pipe wall thinning defect sizes using microwave NDT
AU - Guo, Yijun
AU - Chen, Guanren
AU - Katagiri, Takuya
AU - Yusa, Noritaka
AU - Hashizume, Hidetoshi
N1 - Funding Information:
This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.
Publisher Copyright:
© 2022 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - back propagation neural network
KW - microwaves
KW - resonant frequency
KW - signal processing
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U2 - 10.1080/10589759.2022.2051505
DO - 10.1080/10589759.2022.2051505
M3 - Article
AN - SCOPUS:85126349506
SN - 1058-9759
VL - 37
SP - 737
EP - 753
JO - Nondestructive Testing and Evaluation
JF - Nondestructive Testing and Evaluation
IS - 6
ER -