中国石油大学学报（自然科学版）2018，Vol.42Issue(3)：131-136,6.DOI:10.3969/j.issn.1673-5005.2018.03.016
基于人工神经网络的连续油管疲劳寿命预测
Fatigue life prediction of coiled tubings based on artificial neural network
摘要
Abstract
In the study,we use the artificial neural network theory to predict the low cycle fatigue life of coiled tubings with surface defects. Based on the self-organizing feature map(SOFM) and radial basis function(RBF) neural networks,and con-sidering the effect of coiled tubing surface imperfections,a hybrid network model is built for predicting the life of coiled tub-ings. Using the self-organizing clustering ability of the SOFM neural network,in the model we classify the sample data,and the classification centers and corresponding weight vectors are transmitted to the RBF neural network,as the centers of RBF activa-tion function,and then we can predict the working lives of coiled tubings by the nonlinear approximation ability of RBF neural network. The result shows that the hybrid network model is superior to BP neural network in accuracy and stability.关键词
连续油管/疲劳寿命/表面缺陷/人工神经网络Key words
coiled tubing/fatigue life/surface defect/artificial neural network分类
能源科技引用本文复制引用
于桂杰,赵崇,迟建伟,张佳兴..基于人工神经网络的连续油管疲劳寿命预测[J].中国石油大学学报（自然科学版）,2018,42(3):131-136,6.基金项目
国家科技重大专项(2015ZX05072004) （2015ZX05072004）
