Improving Pipeline Magnetic Flux Leakage (MFL) Detection Performance With Mixed Attention Mechanisms (AMs) and Deep Residual Shrinkage Networks (DRSNs)-Reference-Cited by-同舟云学术

Improving Pipeline Magnetic Flux Leakage (MFL) Detection Performance With Mixed Attention Mechanisms (AMs) and Deep Residual Shrinkage Networks (DRSNs)

Published:2024-02-15 Issue:4 Volume:24 Page:5162-5171
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Zhang Luying¹,Bian Yuchen¹^ORCID,Jiang Peng²,Huang Yang¹,Liu Ying¹

Affiliation:

1. School of Petroleum Engineering, Northeast Petroleum University, Daqing, China

2. School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing, China

Funder

Training Plan of Young Innovative Talents in Heilongjiang Province Ordinary Undergraduate Universities Project

Natural Science Foundation of Heilongjiang Province

Postdoctoral Research Foundation of Heilongjiang Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10433867/10380537.pdf?arnumber=10380537

Reference24 articles.

1. Long-term stability of guided wave electromagnetic acoustic transducer systems

2. A Simplified Lift-Off Correction for Three Components of the Magnetic Flux Leakage Signal for Defect Detection

3. Development of Permanently Installed Magnetic Eddy Current Sensor for Corrosion Monitoring of Ferromagnetic Pipelines

4. An Improved Method for Pipeline Leakage Localization With a Single Sensor Based on Modal Acoustic Emission and Empirical Mode Decomposition With Hilbert Transform

5. Deep Learning for Magnetic Flux Leakage Detection and Evaluation of Oil & Gas Pipelines: A Review

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