Magnetic flux leakage defect size estimation method based on physics-informed neural network

Author:

Xiong Yi12,Liu Shuai12,Hou Litao3,Zhou Taotao12ORCID

Affiliation:

1. College of Safety and Ocean Engineering, China University of Petroleum-Beijing, Beijing, People's Republic of China

2. Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, Beijing, People's Republic of China

3. Dushanzi Oil and Gas Branch, West Pipeline, PipeChina, Karamay, People's Republic of China

Abstract

Magnetic flux leakage (MFL) is a magnetic method of non-destructive testing for in-pipe defect detection and sizing. Despite the fact that recent developments in machine learning have revolutionized disciplines like MFL defect size estimation, the most current methods for quantifying pipeline defects are primarily data-driven, which may violate the underlying physical knowledge. This paper proposes a physics-informed neural network-based method for MFL defect size estimation. The training process of neural network is guided by the MFL data and the physical constraints that is mathematically represented by the magnetic dipole model. We use synthetic MFL data produced by a virtual MFL testing of pipeline defects to validate the proposed method through a comparison to purely data-driven neural networks and support vector machines. The findings imply that the physics-informed strategy can both improve predictive accuracy and mitigate physical violations in MFL testing, providing us with a better knowledge of how neural networks perform in defect size estimation. This article is part of the theme issue ‘Physics-informed machine learning and its structural integrity applications (Part 2)’.

Funder

National Natural Science Foundation of China

Young Elite Scientist Sponsorship Program by Beijing Association for Science and Technology

Science Foundation of China University of Petroleum, Beijing

Publisher

The Royal Society

Subject

General Physics and Astronomy,General Engineering,General Mathematics

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