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

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

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URL: https://www.researchgate.net/publication/380037333...

Abstract

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.

Item Type:	Article
Keyword:	Genetic Optimization; Magnetic Leakage Detection; BP Neural Network; Steel Pipe Defects
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science T Technology > TD Environmental technology. Sanitary engineering > TD1-1066 Environmental technology. Sanitary engineering > TD201-500 Water supply for domestic and industrial purposes > TD481-493 Water distribution systems
Department:	FACULTY > Faculty of Engineering
Depositing User:	ABDULLAH BIN SABUDIN -
Date Deposited:	19 Nov 2024 15:51
Last Modified:	19 Nov 2024 15:51
URI:	https://eprints.ums.edu.my/id/eprint/41952

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