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Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks

The aging infrastructure of petroleum and natural gas pipelines poses a threat to national economies, necessitating precise defect detection for safety and efficiency. To enhance the accuracy of predicting pipeline defect sizes, this study introduces a magnetic leakage detection system, employing Ba...

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Main Authors: Wang Jie, Mohd. Kamal Mohd. Shah, Choong Wai Heng, Nahiyan Al-Azad
Format: Article
Language:English
English
Published: Zibeline International Publishing Sdn. Bhd. 2024
Subjects:
QA75.5-76.95 Electronic computers. Computer science
TD481-493 Water distribution systems
Online Access:https://eprints.ums.edu.my/id/eprint/41952/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/41952/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/41952/
http://dx.doi.org/10.24191/jmeche.v21i2.26256
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spelling my.ums.eprints.419522024-11-19T07:51:12Z https://eprints.ums.edu.my/id/eprint/41952/ Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks Wang Jie Mohd. Kamal Mohd. Shah Choong Wai Heng Nahiyan Al-Azad QA75.5-76.95 Electronic computers. Computer science TD481-493 Water distribution systems The aging infrastructure of petroleum and natural gas pipelines poses a threat to national economies, necessitating precise defect detection for safety and efficiency. To enhance the accuracy of predicting pipeline defect sizes, this study introduces a magnetic leakage detection system, employing Backpropagation (BP) neural networks optimized with genetic algorithms. Traditional BP networks face challenges, including parameter determination and slow convergence, addressed through genetic algorithms' global search capabilities. Simulated data are generated using ANSYS software by using models of semi-circular defects in steel pipes, producing magnetic leakage signals of varying intensities. MATLAB was used to construct both standard BP and genetically optimized BP neural networks. Results show that the latter significantly reduces computational errors, demonstrating improved accuracy in defect dimension prediction. The approach contributes to overcoming nonuniqueness in the recognition process and the complex nonlinear relationship between magnetic signals and defect size parameters. The study offers a guided approach for selecting BP neural network parameters, enhancing practicality. Simulations validate the method's effectiveness, indicating low workload and high reliability. This research provides a meaningful advancement in the detection of defects in long-distance pipelines, impacting the safety and efficiency of petroleum and natural gas transportation. Zibeline International Publishing Sdn. Bhd. 2024 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/41952/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/41952/2/FULL%20TEXT.pdf Wang Jie and Mohd. Kamal Mohd. Shah and Choong Wai Heng and Nahiyan Al-Azad (2024) Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks. Journal of Mechanical Engineering Research and Developments, 21 (2). pp. 1-17. ISSN 1024-1752 http://dx.doi.org/10.24191/jmeche.v21i2.26256
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic QA75.5-76.95 Electronic computers. Computer science
TD481-493 Water distribution systems
spellingShingle QA75.5-76.95 Electronic computers. Computer science
TD481-493 Water distribution systems
Wang Jie
Mohd. Kamal Mohd. Shah
Choong Wai Heng
Nahiyan Al-Azad
Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks
description The aging infrastructure of petroleum and natural gas pipelines poses a threat to national economies, necessitating precise defect detection for safety and efficiency. To enhance the accuracy of predicting pipeline defect sizes, this study introduces a magnetic leakage detection system, employing Backpropagation (BP) neural networks optimized with genetic algorithms. Traditional BP networks face challenges, including parameter determination and slow convergence, addressed through genetic algorithms' global search capabilities. Simulated data are generated using ANSYS software by using models of semi-circular defects in steel pipes, producing magnetic leakage signals of varying intensities. MATLAB was used to construct both standard BP and genetically optimized BP neural networks. Results show that the latter significantly reduces computational errors, demonstrating improved accuracy in defect dimension prediction. The approach contributes to overcoming nonuniqueness in the recognition process and the complex nonlinear relationship between magnetic signals and defect size parameters. The study offers a guided approach for selecting BP neural network parameters, enhancing practicality. Simulations validate the method's effectiveness, indicating low workload and high reliability. This research provides a meaningful advancement in the detection of defects in long-distance pipelines, impacting the safety and efficiency of petroleum and natural gas transportation.
format Article
author Wang Jie
Mohd. Kamal Mohd. Shah
Choong Wai Heng
Nahiyan Al-Azad
author_facet Wang Jie
Mohd. Kamal Mohd. Shah
Choong Wai Heng
Nahiyan Al-Azad
author_sort Wang Jie
title Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks
title_short Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks
title_full Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks
title_fullStr Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks
title_full_unstemmed Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks
title_sort defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks
publisher Zibeline International Publishing Sdn. Bhd.
publishDate 2024
url https://eprints.ums.edu.my/id/eprint/41952/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/41952/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/41952/
http://dx.doi.org/10.24191/jmeche.v21i2.26256
_version_ 1817843830277275648
score 13.244413

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