摘要
【目的】埋地管道在石油、天然气等资源运输方面发挥着重要作用,但受周围土壤环境影响,埋地管道点蚀缺陷时有发生,严重影响管道运行可靠性。弱磁检测技术作为一种新型的无损检测技术,具有操作简易、灵敏度高、适应性好等优势,被广泛应用于埋地油气管道检测领域。弱磁检测的数据分散性与相关样本数据过少的问题导致缺陷反演精度不足。在实际工程中,点蚀缺陷不易发现,检测难度较大,且现有研究鲜有涉及缺陷角度的预测。【方法】将立方混沌映射与自适应惯性权重的鲸鱼优化算法(Chaotic Improved Whale Optimization Algorithm,CIWOA)与向后传播BP神经网络相结合,针对点蚀缺陷尺寸与角度提出基于弱磁检测的埋地管道点蚀缺陷反演方法。首先,通过管道弱磁检测试验分析不同点蚀缺陷对弱磁信号特征的影响,选取反演参数并构建数据集,利用Smote算法进行数据增强;其次,采用核主成分分析(Kernel Principal Components Analysis,KPCA)进行数据降维,确定点蚀缺陷尺寸及角度的主成分;最后,建立CIWOA-BP模型预测埋地管道点蚀缺陷尺寸及角度,并与其他模型预测结果进行对比。【结果】CIWOA-BP模型可以实现埋地管道点蚀缺陷尺寸及角度的准确预测,相比于WOA-BP(WhaleOptimizationAlgorithm-BackPropagation)、BP、RF(RandomForest)以及SVM(SupportVector Machine)模型,CIWOA-BP模型的平均绝对百分比误差、均方误差、平均绝对误差值更小,绝对系数R2值更接近于1,表现出更高的预测精度。【结论】在此提出的基于弱磁检测的埋地管道点蚀缺陷反演方法可以实现点蚀缺陷的准确反演,对于保障埋地管道安全运输具有良好的应用前景。但由于试验条件受限,该研究未能全面考虑弱磁信号所有影响因素,后续需进一步改进。
[Objective]Buried pipelines play a vital role in the transportation of resources such as petroleum and natural gas.However,pitting defects frequently occur in these pipelines exposed to surrounding soil environments,seriously compromising their reliability.As a novel non-destructive testing technique,weak magnetic testing has been widely adopted in the detection of buried oil and gas pipelines due to its advantages,including simplicity of operation,high sensitivity,and strong adaptability.Currently,the dispersion of weak magnetic testing data and a lack of relevant sample data hinder the accuracy of defect inversion.Detecting pitting defects in engineering practices presents significant challenges.Additionally,previous research has overlooked the prediction of defect angles.[Methods]This study proposes a methodology for inverting pitting defects in buried pipelines based on weak magnetic testing,utilizing a combination of the Chaotic Improved Whale Optimization Algorithm(CIWOA)and the BP neural network,with a focus on the size and angle of the defects.First,the impact of various pitting defects on the characteristics of weak magnetic signals was analyzed through experimental weak magnetic testing on pipelines.The results were then used to select inversion parameters and establish data sets,which were enhanced using the Synthetic Minority Oversampling Technique(SMOTE)algorithm.Next,Kernel Principal Component Analysis(KPCA)was applied to reduce the dimensionality of the data and identify the principal components contributing to the sizes and angles of pitting defects.Finally,a CIWOA-BP model was developed to predict the size and angle of pitting defects in buried pipelines.A comparison of the prediction results from the proposed model was conducted against those from other existing models.[Results]The results demonstrate the accuracy of the CIWOA-BP model in predicting the size and angle of pitting defects in buried pipelines.With smaller values of mean absolute percentage error(MAPE),mean squared error(MSE),and mean absolute error(MAE),along with the coefficient of determination R2 closer to 1 compared to WOA-BP(Whale Optimization Algorithm-Back Propagation),BP,RF(Random Forest),and SVM(Support Vector Machine),the CIWOA-BP model shows superior prediction accuracy for both the sizes and angles of pitting defects.[Conclusion]Therefore,the proposed method enables accurate inversion of pitting defects in buried pipelines using weak magnetic testing,presenting promising application prospects for ensuring the safe transportation of these pipelines.
作者
施宁
孙祥龙
苗兴园
王维斌
赵弘
宁志远
SHI Ning;SUN Xianglong;MIAO Xingyuan;WANG Weibin;ZHAO Hong;NING Zhiyuan(PipeChina Institute of Science and Technology;College of Mechanical and Transportation Engineering,China University of Petroleum(Beijing))
出处
《油气储运》
北大核心
2025年第8期890-898,共9页
Oil & Gas Storage and Transportation
基金
国家自然科学基金资助项目“海底油气管道封堵致振机理及气动减振控制方法研究”,51575528
国家管网集团科学技术研究总院分公司科技攻关项目“基于数据驱动的力磁耦合模型”,GWHT20230044324。
关键词
弱磁检测
埋地管道
点蚀缺陷反演
缺陷角度
数据降维
CIWOA-BP模型
weak magnetic testing
buried pipeline
inverting pitting defect
defect angle
dimensionality reduction of data
Chaotic Improved Whale Optimization Algorithm-Back Propagation(CIWOA-BP)model
作者简介
施宁,男,1987年生,高级工程师,2014年硕士毕业于中国石油大学(北京)油气储运工程专业,现主要从事管道安全与维护专业方向的研究工作。地址:天津市经济技术开发区洞庭一街4号,300150。电话:022-61187799。Email:564770359@qq.com;通信作者:赵弘,女,1974年生,教授,博士生导师,2003年博士毕业于西安交通大学机械电子工程专业,现主要从事机电系统及管道智能机器人设计及控制技术、智能制造3D打印技术研究工作。地址:北京市昌平区府学路18号,102299。电话:13522390529。Email:hzhao_cn@163.com。
