Reconstruction of 3-D pipeline corrosion defect profile from MFL signals with deep learning approach

Jiatong Ling; Xiang Peng; Kevin Siggers; Matthias Peussner; Rakiba Rayhana; Zheng Liu

doi:10.1117/12.3010024

9 May 2024 Reconstruction of 3-D pipeline corrosion defect profile from MFL signals with deep learning approach

Jiatong Ling, Xiang Peng, Kevin Siggers, Matthias Peussner, Rakiba Rayhana, Zheng Liu

Proceedings Volume 12952, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE II; 129520A (2024) https://doi.org/10.1117/12.3010024
Event: SPIE Smart Structures + Nondestructive Evaluation, 2024, Long Beach, California, United States

Abstract

Magnetic flux leakage (MFL) is a widely used nondestructive testing technique in pipeline inspection to detect and quantify defects. In pipeline integrity management, the reconstruction of defects from MFL signals plays a critical role in failure pressure prediction and maintenance decision-making. In current research practices, this reconstruction primarily involves the determination of defect dimensions, including length, width, and depth, collectively forming a rectangular box. However, this box-based representation potentially leads to conservative assessments of pipeline integrity. To fine-scale the reconstruction results and provide detailed defect information for the integrity assessment, a 3-D reconstruction model for pipeline corrosion defects from MFL signals is proposed. In detail, the deep neural network is established to capture the nonlinear relationship between the MFL signals and 3-D defect profiles. In contrast to the limited insights offered by the box profile, the reconstructed 3-D profile in this paper enables more detailed metal loss geometry. The experiments using field pipeline in-line inspection data demonstrate promising results on both morphology and depth prediction.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiatong Ling, Xiang Peng, Kevin Siggers, Matthias Peussner, Rakiba Rayhana, and Zheng Liu "Reconstruction of 3-D pipeline corrosion defect profile from MFL signals with deep learning approach", Proc. SPIE 12952, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE II, 129520A (9 May 2024); https://doi.org/10.1117/12.3010024

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available