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28 July 2023 Failure prediction of weld defects in long distance pipeline
Xiao-xing Feng, Ye Fan
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Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 127564U (2023) https://doi.org/10.1117/12.2686238
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China
Abstract
As an important national energy transportation channel, long distance oil and gas pipeline plays a very important role in the industrial development. In order to ensure the safe operation of pipelines, efficient and accurate prediction of the risk level of long-distance pipelines is essential. Aiming at this practical problem, this paper proposes a failure prediction method of girth weld based on neural network on the basis of analyzing the existing failure prediction methods and models of girth weld. Based on the division of pipeline risk prediction in pipeline integrity management standard and the investigation of factors affecting the failure of girth weld, a neural network model of girth weld failure prediction was constructed. In this paper, the training method of neural network is analyzed and the neural network model suitable for failure risk prediction of girth weld is determined theoretically. The proposed neural network model for failure risk prediction is verified by the actual operation data of a pipeline. The actual calculation shows that the prediction accuracy of high and medium risk girth weld failure risk can reach 100%, 94.1% and 85.7%, respectively.
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Xiao-xing Feng and Ye Fan "Failure prediction of weld defects in long distance pipeline", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 127564U (28 July 2023); https://doi.org/10.1117/12.2686238
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KEYWORDS
Failure analysis
Neural networks
Artificial neural networks
Education and training
Neurons
Finite element methods
Computing systems
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