摘要
针对传统腐蚀管道预测方法适应性差及精度低等问题,提出一种随机森林算法(RF)、思维进化法(MEA)及Elman相结合的模型(即RF-MEA-Elman模型)。首先采用RF对管道数据预处理,运用MEA对Elman神经网络的权值和阈值参数进行寻优,以此建立腐蚀管道剩余寿命组合预测模型。选取某一管段为例,借助MATLAB进行仿真训练与预测。结果表明,该模型与其他两种传统单一模型相比误差小且有更高的预测精度及泛化能力,为管道剩余寿命研究提供了新思路,也为油气输送系统风险防范和维修管理提供了参考依据。
Aiming at the problems of poor adaptability and low accuracy of traditional prediction methods for corroded pipelines,a model combining random forest algorithm(RF),evolution of mind(MEA)and elman(RF-MEA-Elman model)was proposed:First,RF was used for pipelines data preprocessing,using MEA to optimize the weight and threshold parameters of the Elman neural network,so as to establish a combined prediction model for the remaining life of the corroded pipeline.Taking a certain pipe section as an example,the simulation training and prediction were carried out with the help of MATLAB.The results show that compared with the other two traditional single models,this model has smaller error and higher prediction accuracy and generalization ability,which provides a useful tool for the study of the remaining life of the pipeline.The new thinking also provides a reference for the risk prevention and maintenance management of the oil and gas transportation system.
作者
骆正山
张维宏
王小完
张新生
LUO Zhengshan;ZHANG Weihong;WANG Xiaowan;ZHANG Xinsheng(School of Management,Xi'an University of Architecture and Technology,Xi'an 710055,China)
出处
《热加工工艺》
北大核心
2023年第8期39-43,共5页
Hot Working Technology
基金
国家自然科学基金资助项目(41877527)
陕西省社科基金项目(2018S34)。
