Research on RBF Neural Network Prediction of Oil and Gas Pipe Dent Depth

被引：0

作者：

Jia Guanwei ^{[1
]}

Cai Maolin ^{[1
]}

Du Bingtong ^{[3
]}

Li Rui ^{[1
,2
]}

Shi Yan ^{[1
]}

机构：

[1] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China

[2] Petrochina Pipeline Co, Langfang 065000, Peoples R China

[3] Sch Mech Engn & Automat, Beijing, Peoples R China

来源：

2016 IEEE/CSAA INTERNATIONAL CONFERENCE ON AIRCRAFT UTILITY SYSTEMS (AUS) | 2016年

关键词：

geometry inspection equipment; oil and gas pipelines; RBF neural network; dent depth predict;

D O I：

暂无

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

Oil and gas transportation in pipeline plays an important role in the lifeline of national economy, industrial production and daily life. In China, aging phenomenon of oil and natural gas pipeline is widespread in the existing pipeline. Therefore, the safety of the pipeline has been concerned. It is a great significance for forecasting dent depth of transportation pipeline accurately. In this paper, according to the complicated radial displacement and the characteristic of RBF neural network, the model of RBF neural was constructed combining with pipeline dent depth data pipeline. The RBF model was applied to predict dent depth in the pipelines, it was testified that the RBF neural network model has higher prediction accuracy than BP neural network model.

引用

页码：335 / 339

页数：5

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