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
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
收藏
页码:335 / 339
页数:5
相关论文
共 50 条
  • [1] Research on oil and gas pipeline defect recognition based on IPSO for RBF neural network
    Zhang, He
    Yu, Xiaojie
    SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS, 2018, 20 : 203 - 209
  • [2] Prediction of corrosion failure probability of buried oil and gas pipeline based on an RBF neural network
    Zhao, Lexin
    Luo, Zhengshan
    Deng, Guangya
    Shi, Victor
    FRONTIERS IN EARTH SCIENCE, 2023, 11
  • [3] Structural Performance of Oil and Gas Pipe with Dent Defect
    Ghaednia, Hossein
    Das, Sreekanta
    JOURNAL OF PIPELINE SYSTEMS ENGINEERING AND PRACTICE, 2018, 9 (01)
  • [4] Groundwater depth prediction model based on IABC-RBF neural network
    Shao G.-C.
    Zhang K.
    Wang Z.-Y.
    Wang X.-J.
    Lu J.
    Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science), 2019, 53 (07): : 1323 - 1330
  • [5] Gas Content Prediction Based on GA-RBF Neural Network
    Zhai, Bo
    Shan, Jianfeng
    2010 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-5, 2010, : 3104 - +
  • [6] A research on scenic information prediction method based on RBF neural network
    Li, Jingwen
    Yin, Shouqiang
    Wang, Ke
    INTERNATIONAL CONFERENCE ON INTELLIGENT EARTH OBSERVING AND APPLICATIONS 2015, 2015, 9808
  • [7] Research and prediction of atmospheric pollution based on wavelet and RBF neural network
    Anhui University of Technology and Science, Wuhu 241000, China
    不详
    Xitong Fangzhen Xuebao, 2006, 5 (1411-1413):
  • [8] Prediction of oil gas history based on neural network
    Zhao, Jun
    Xia, Hongquan
    Liu, Hongqi
    Xinan Shiyou Xueyuan Xuebao/Journal of Southwestern Petroleum Institute, 20 (02): : 23 - 26
  • [9] Research on application of wavelet analysis and RBF neural network to prediction of foundation settlement
    Li Chang-dong
    Tang Hui-ming
    Hu Bin
    Li Dong-ming
    Ni Jun
    ROCK AND SOIL MECHANICS, 2008, 29 (07) : 1917 - 1922
  • [10] Prediction research on cutting surface roughness of PBX based on RBF neural network
    Tang, Xian-Jin
    Zhang, Qiu
    Zou, Gang
    Wu, Song
    Liu, Wei
    Yin, Rui
    Binggong Xuebao/Acta Armamentarii, 2014, 35 (02): : 200 - 206