| Item type |
文献 / Documents(1) |
| 公開日 |
2024-09-20 |
| アクセス権 |
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|
アクセス権 |
open access |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 出版社版DOI |
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|
関連識別子 |
https://doi.org/10.1177/14759217241249240 |
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関連名称 |
10.1177/14759217241249240 |
| 出版タイプ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| タイトル |
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|
タイトル |
Depth estimation of pipe wall thinning using multifrequency reflection coefficients of T(0,1) mode-guided waves with supervised multilayer perceptron |
| 著者 |
カツマ, リュウジン
タダ, コウキ
イリグチ, トモヤ
セノオ, コタロウ
コンドウ, シンスケ
石川, 真志
ゴカ, モトキ
西野, 秀郎
|
| 抄録 |
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内容記述 |
This study entailed the development of a novel method for estimating the depth of wall thinning of pipes using multifrequency (30–65 kHz) reflection coefficients (MRCs) of the T(0,1) mode guided waves and a multilayer perceptron (MLP). First, this study established why MRCs are a critical feature of the input layer of the MLP for the defect depth estimation of wall thinning. Further, a mathematical model that can quickly collect large amounts of training data was used to calculate the reflection waveforms. The depths of artificial and actual wall thinning were estimated using the MLP based on the MRCs and the mathematical model. Experiments were conducted using the T(0,1) mode guided waves to obtain the MRCs for 21 artificial and 6 actual wall thinnings to estimate the defect depths. A maximum of 8347 training data points were prepared using the mathematical model. Because the optimization of the MLP strongly depended on the initial weights and biases, 100 random initial values were prepared to evaluate the average estimations and their standard deviations. The classification scheme of the MLP was used, with classification step widths of 0.5 and 0.25 mm. The correct answer rates for the 21 artificial defects were 93% with a tolerance of ±0.5 mm for the 0.5 mm classification scheme; those for the 0.25 mm classification scheme were 89%. For the six actual defects, the correct answer rates were 100% with a tolerance of ±0.5 mm for both the 0.5- and 0.25 mm classification schemes. Sufficiently high correct answer rates were obtained in all the cases. |
| キーワード |
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|
主題 |
Ultrasonic testing |
| キーワード |
|
|
主題 |
guided wave |
| キーワード |
|
|
主題 |
pipe wall thinning |
| キーワード |
|
|
主題 |
artificial intelligence |
| キーワード |
|
|
主題 |
supervised machine learning |
| キーワード |
|
|
主題 |
multilayer perceptron |
| 書誌情報 |
en : Structural Health Monitoring
巻 24,
号 3,
p. 1783-1803,
発行日 2024-07-24
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| 収録物ID |
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収録物識別子タイプ |
ISSN |
|
収録物識別子 |
14759217 |
| 収録物ID |
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収録物識別子タイプ |
ISSN |
|
収録物識別子 |
17413168 |
| 収録物ID |
|
|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA11823338 |
| 出版者 |
|
|
出版者 |
SAGE Publications |
| 権利情報 |
|
|
権利情報 |
R Katsuma, K Tada, T Iriguchi, K Seno, S Kondo, M Ishikawa, M Goka, H Nishino, Depth estimation of pipe wall thinning using multifrequency reflection coefficients of T(0,1) mode-guided waves with supervised multilayer perceptron, Structural Health Monitoring. Copyright © 2024 The Author(s). DOI: 10.1177/14759217241249240. |
| EID |
|
|
識別子 |
413245 |
| 言語 |
|
|
言語 |
eng |
shm_24_3_1783.pdf (5 MB)
