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磁记忆PE全包覆抽油杆SVM应力预测研究

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为探测PE全包覆防腐层抽油杆的应力区域,解决抽油杆状态评估中的技术难点.采集预制点腐蚀、面腐蚀和沟槽腐蚀的PE全包覆防腐层抽油杆的磁记忆信号,并用支持向量机(SVM)研究信号特征值与缺陷应力值,以及应力状态之间的关系,建立应力状态预测模型,引入粒子群优化算法优化SVM模型参数.通过优化,用该模型预测抽油杆应力状态的平均误差仅为3.638%.结果表明:基于粒子群优化算法和7种磁记忆信号特征量建立的支持向量机模型准确率较高,适用于PE全包覆防腐层抽油杆的应力状态预测.
Magnetic memory PE full-encapsulation sucker rod SVM stress prediction research
In order to detect the stress region of PE fully clad anti-corrosion layer pumping rods and solve the technical difficulties in the condition assessment of pumping rods.The magnetic memory signals of prefabricated point corrosion,face corrosion and groove corrosion of PE fully clad anticorrosion layer sucker rods were collected,and the relationship between the signal eigenvalues and the defect stress values,as well as the stress state was investigated by using support vector machines(SVMs)to establish a stress state prediction model,and a particle swarm optimisation(PSO)algorithm was introduced to optimise the parameters of the SVM model.Through the optimisation,the average error of predicting the stress state of the sucker rod using this model is only 3.638% .The results show that the support vector machine model established based on particle swarm optimization algorithm and seven kinds of magnetic memory signal feature quantities has high accuracy and is suitable for predicting the stress state of pumping rods with PE fully clad anti-corrosion layer.

PE fully coated anti-corrosion rodsupport vector machinesmetallic magnetic memorysignal characteristic quantityparticle swarm optimization

周先军、彭煜轩、刘瑾、刘延峰、杨勇、王瑞琦

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中国石油大学(华东)机电工程学院,山东 青岛 266000

中国石油化工股份有限公司胜利油田分公司技术检测中心,山东 东营 257000

PE全包覆防腐层抽油杆 支持向量机 金属磁记忆 信号特征量 粒子群优化

2025

兵器材料科学与工程
中国兵工学会 中国兵器工业集团第52研究所

兵器材料科学与工程

北大核心
影响因子:0.334
ISSN:1004-244X
年,卷(期):2025.48(1)