文献类型：期刊文献

英文题名：Research on Corrosion Rate Prediction of Buried Pipeline Based on KPCA-Improved PSO-BP Neural Network Model

作者：Yu, Yang[1]; Sun, Dongliang[2]

机构：[1] East China University of Science and Technology, School of Mechanical Engineering, Shanghai, 200237, China; [2] East China University of Science and Technology, School of Resources and Environmental Engineering, Shanghai, 200237, China

年份：2023

起止页码：557

外文期刊名：2023 4th International Conference on Mechatronics Technology and Intelligent Manufacturing, ICMTIM 2023

收录：EI(收录号：20234214905378)

语种：英文

外文关键词：Forecasting - Neural networks - Pipeline corrosion - Pipelines - Underground corrosion

摘要：Corrosion will damage the structure of buried pipeline and lead to its failure. Corrosion rate varies with different soil properties. In order to predict the corrosion rate of buried pipeline accurately and reliably, KPCA- improved PSO-BPNN prediction model is proposed. In this paper, a buried oil pipeline section in Shaanxi Province is selected for research. Firstly, an 8-dimensional external corrosion index system is constructed. Secondly, in order to improve the fitting effect and remove redundant information, KPCA is introduced for preprocessing to obtain a 6-dimensional index system. Thirdly, BPNN is optimized by PSO algorithm with improved weight parameters, and corrosion rate is predicted through training. Finally, by comparing the prediction accuracy of single BPNN, KPCA-BPNN and KPCA-improved PSO-BPNN model, it is found that the accuracy of KPCA-improved PSO-BPNN model can reach 94.73%, which is 13.26% and 8.04% higher than the previous two models respectively. Therefore, the proposed new model can better meet the actual engineering requirements. ? 2023 IEEE.