Achievement Journal NDT&E Int. Student

論文掲載決定(NDT&E International誌)!!

更新日:

何とこの1週間で3つ目の論文受理通知です。非破壊検査に関する欧文論文誌NDT&E International誌にInverse analysis of low frequency electromagnetic signals for sizing local wall thinning using a multivariate probabilistic model (多変量確率モデルを用いた低周波電磁場からの局所減肉の形状推定)というタイトルで投稿した配管減肉評価に関する論文が受理されました。

論文の内容は、炭素鋼配管の内面に発生した腐食減肉の形状を、低周波電磁場を用いた非破壊検査信号から定量的に評価するための数値解析アルゴリズムを開発し、その有効性を実験室レベルの測定試験信号を用いて示した、というものです。配管減肉を評価するという技術自体は実用化に至っているものも含めて少なからずありますが、開発したアルゴリズムは結果の信頼性も含めて評価できること、また信号特徴量の相関性を利用することでその際の精度を向上させている、といった特徴があります。

簡単ですがこちらが実験体系。内面に局所的な減肉を加工した配管(加工大変)が電磁場を乱す様子を測定します。

で、ややこしい数式は飛ばしまして、こちらが推定結果。肉厚はこれくらいの範囲にあると推定される、といった感じの評価ができるという特徴があります。以前のアルゴリズムに比べてずいぶん精度は向上しました。

こちらは測定時の周方向位置がずれた場合の影響。あんまり影響がないのは驚きです。

論文の概要は以下。

Author: Haicheng Song, Noritaka Yusa

Title: Inverse analysis of low frequency electromagnetic signals for sizing local wall thinning using a multivariate probabilistic model (多変量確率モデルを用いた低周波電磁場からの局所減肉の形状推定)

Abstract: Local wall thinning is a common form of degradation in carbon steel pipes, and a low frequency electromagnetic method is proposed in this study to inspect such wall thinning defects. In addition, an appropriate method needs to be developed to solve the inverse problem, which is to estimate defect size based on the inspection signal. Resolving the inverse problem of nondestructive inspection usually involves a machine learning algorithm and training data should be large and realistic to enable the algorithm to produce an accurate and reliable estimate of defect size. However, the acquisition of such training data is sometimes time-consuming and costly. Therefore, this study aims to estimate the size of local wall thinning based on the inspection signal by developing a new method to generate training data for a machine learning algorithm. With the aid of signals obtained from numerical simulation, a multivariate probabilistic model is proposed to infer a joint distribution over features extracted from measured multi-frequency signals from the low frequency electromagnetic inspection method. The joint distribution is subsequently leveraged to quickly generate sufficient training data for a Gaussian process regression algorithm that uses the signal features as the input to estimate defect size. The multivariate probabilistic model is proved able to reasonably characterize the joint distribution over the features. Moreover, the trained algorithm has been validated by experimental data and it is confirmed it can be used to estimate the residual thickness of a pipe wall with errors within tolerance and high reliability even when the lift-off is
changed.

-Achievement, Journal, NDT&E Int., Student
-

Copyright© Yusa Laboratory , 2022 All Rights Reserved Powered by STINGER.