IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/3681032.html
   My bibliography  Save this article

Principal Component Analysis Based Dynamic Fuzzy Neural Network for Internal Corrosion Rate Prediction of Gas Pipelines

Author

Listed:
  • Xiaoxu Chen
  • Linyuan Wang
  • Zhiyu Huang

Abstract

Aiming at the characteristics of the nonlinear changes in the internal corrosion rate in gas pipelines, and artificial neural networks easily fall into a local optimum. This paper proposes a model that combines a principal component analysis (PCA) algorithm and a dynamic fuzzy neural network (D-FNN) to address the problems above. The principal component analysis algorithm is used for dimensional reduction and feature extraction, and a dynamic fuzzy neural network model is utilized to perform the prediction. The study implementing the PCA-D-FNN is further accomplished with the corrosion data from a real pipeline, and the results are compared among the artificial neural networks, fuzzy neural networks, and D-FNN models. The results verify the effectiveness of the model and algorithm for inner corrosion rate prediction.

Suggested Citation

  • Xiaoxu Chen & Linyuan Wang & Zhiyu Huang, 2020. "Principal Component Analysis Based Dynamic Fuzzy Neural Network for Internal Corrosion Rate Prediction of Gas Pipelines," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-9, September.
    • Handle: RePEc:hin:jnlmpe:3681032
    DOI: 10.1155/2020/3681032
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2020/3681032.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2020/3681032.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2020/3681032?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Xinhong & Jia, Ruichao & Zhang, Renren & Yang, Shangyu & Chen, Guoming, 2022. "A KPCA-BRANN based data-driven approach to model corrosion degradation of subsea oil pipelines," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    2. Yang, Jianfeng & Suo, Guanyu & Chen, Liangchao & Dou, Zhan & Hu, Yuanhao, 2023. "Prediction method of key corrosion state parameters in refining process based on multi-source data," Energy, Elsevier, vol. 263(PA).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:3681032. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.