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Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks / Wang Jie ... [et al.]

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. Shah, Mohd. Kamal, Choong, Wai Heng, Al-Azad, Nahiyan
Format: Article
Language:English
Published: Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM) 2024
Subjects:
TP Chemical technology
Online Access:https://ir.uitm.edu.my/id/eprint/94419/1/94419.pdf
https://ir.uitm.edu.my/id/eprint/94419/
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spelling my.uitm.ir.944192024-04-29T04:31:10Z https://ir.uitm.edu.my/id/eprint/94419/ Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks / Wang Jie ... [et al.] jmeche Wang, Jie Mohd. Shah, Mohd. Kamal Choong, Wai Heng Al-Azad, Nahiyan TP Chemical technology 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 non-uniqueness 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. Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM) 2024-04 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/94419/1/94419.pdf Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks / Wang Jie ... [et al.]. (2024) Journal of Mechanical Engineering (JMechE) <https://ir.uitm.edu.my/view/publication/Journal_of_Mechanical_Engineering_=28JMechE=29/>, 21 (2): 5. pp. 69-84. ISSN 1823-5514 ; 2550-164X
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic TP Chemical technology
spellingShingle TP Chemical technology
Wang, Jie
Mohd. Shah, Mohd. Kamal
Choong, Wai Heng
Al-Azad, Nahiyan
Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks / Wang Jie ... [et al.]
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 non-uniqueness 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. Shah, Mohd. Kamal
Choong, Wai Heng
Al-Azad, Nahiyan
author_facet Wang, Jie
Mohd. Shah, Mohd. Kamal
Choong, Wai Heng
Al-Azad, Nahiyan
author_sort Wang, Jie
title Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks / Wang Jie ... [et al.]
title_short Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks / Wang Jie ... [et al.]
title_full Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks / Wang Jie ... [et al.]
title_fullStr Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks / Wang Jie ... [et al.]
title_full_unstemmed Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks / Wang Jie ... [et al.]
title_sort defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks / wang jie ... [et al.]
publisher Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM)
publishDate 2024
url https://ir.uitm.edu.my/id/eprint/94419/1/94419.pdf
https://ir.uitm.edu.my/id/eprint/94419/
_version_ 1797926495159582720
score 13.211869

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