NDLI: Defect Signal Detection of Station Process Pipelines Based on the BP Neural Network

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Defect Signal Detection of Station Process Pipelines Based on the BP Neural Network

Content Provider	Scilit
Author	Jiang, Yi Liu, Yuting Yu, Dongliang Li, Feng Chen, Ge Di, Xin
Copyright Year	2021
Description	Journal: Iop Conference Series: Earth and Environmental Science In recent years, leakage, rupture, and perforation accidents in oil and gas pipelines caused by corrosion have increased significantly. Therefore, the online, nondestructive testing of oil and gas pipelines has become essential to maintain their structural integrity. Ultrasonic guided wave detection technology presents advantages, such as high detection efficiency and complete coverage of the pipeline body, which is conducive to popularization and application. This paper considered the natural gas transmission station as the research object, while ultrasonic guided wave technology was used for detection. Ultrasonic guided wave equipment was used to detect the field process pipeline and experimental pipeline, obtaining data samples of the defects and welds. Defects were automatically identified via data processing by combining the characteristic signal method and the BP neural network. The results indicated that the neural network displayed a recognition accuracy of 80.9% for the features and defects in the test samples. By combining the characteristic signal method and the BP neural network, the defect recognition technology can reduce the subjective influence of inspectors while improving recognition efficiency and accuracy.
Related Links	https://iopscience.iop.org/article/10.1088/1755-1315/804/4/042022/pdf
ISSN	17551307
e-ISSN	17551315
DOI	10.1088/1755-1315/804/4/042022
Journal	Iop Conference Series: Earth and Environmental Science
Issue Number	4
Volume Number	804
Language	English
Publisher	IOP Publishing
Publisher Date	2021-07-01
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Earth and Environmental Science Bp Neural Network
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Physics and Astronomy Environmental Science

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