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

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  • 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
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    Cited by:

    1. 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).
    2. 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).

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