PDF下载

[1]施宁孙祥龙苗兴园王维斌赵弘宁志远.基于弱磁检测的埋地管道点蚀缺陷反演方法[J].油气储运,2025,44(06):1-14.
　Ning Shi,Xianglong Sun,Xingyuan Miao,et al.Research on the inversion method of pitting corrosion defects in buried pipelines based on weak magnetic detection[J].Oil & Gas Storage and Transportation,2025,44(06):1-14.

点击复制

基于弱磁检测的埋地管道点蚀缺陷反演方法

《油气储运》[ISSN:1000-8241/CN:13-1093/TE] 卷: 44 期数: 2025年06期页码: 1-14 栏目: 出版日期: 2025-06-25

Title:: Research on the inversion method of pitting corrosion defects in buried pipelines based on weak magnetic detection

作者:: 施宁孙祥龙苗兴园王维斌赵弘宁志远

Author(s):: Ning Shi; Xianglong Sun; Xingyuan Miao; Weibin Wang; Hong Zhao; Zhiyuan Ning

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

Keywords:: weak magnetic testing; buried pipeline; inverting pitting defect; defect angle; dimensionality reduction of data; Cubic Chaotic Mapping-based Adaptive Whale Optimization Algorithm-Back Propagation (CIWOA-BP) model

文献标志码:: A

摘要:: 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，但在周围土壤环境的作用下，埋地管道点蚀缺陷时有发生，严重影响管道的可靠性。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。目前由于弱磁检测数据的分散性及相关样本数据过少，导致缺陷反演精度有待提高。在实际工程中，点蚀缺陷不易发现，检测难度较大，并且当前研究未涉及缺陷角度的预测。【方法】将立方混沌映射和自适应惯性权重的鲸鱼优化算法（Cubic Chaotic Mapping-based Adaptive Whale Optimization Algorithm，CIWOA）与向后传播（Back Propagation，BP）神经网络相结合，针对点蚀缺陷尺寸及角度，提出了基于弱磁检测的埋地管道点蚀缺陷反演方法。首先，通过管道弱磁检测实验分析不同点蚀缺陷对弱磁信号特征的影响，进行反演参数选取并构建数据集，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模型进行埋地管道点蚀缺陷尺寸及角度预测，并与其他模型预测结果进行对比。【结果】结果表明，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，相比于其他模型，CIWOA-BP模型的MAPE、MSE、MAE数值更小，R²数值更接近于1，CIWOA-BP模型在点蚀缺陷尺寸和角度方面都表现出了更高的预测精度。【结论】因此，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，这对于保证埋地管道安全运输具有良好的应用前景。

Abstract:: Abstract: [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 Cubic Chaotic Mapping-based Adaptive Whale Optimization Algorithm (CIWOA) and the Back Propagation (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 an R² value closer to 1 compared to other models, 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.

相似文献/References:

[1]周祥.埋地钢质管道外涂层缺陷的安全质量分级评价[J].油气储运,2011,30(04):299.[doi:10.6047/j.issn.1000-8241.2011.04.017]
　Zhou Xiang.Safety quality grading evaluation on the external coating defects of buried steel pipeline[J].Oil & Gas Storage and Transportation,2011,30(06):299.[doi:10.6047/j.issn.1000-8241.2011.04.017]
[2]付吉强,吴明,杜明俊,等.埋地管道周围土壤湿热耦合相变过程的数值计算[J].油气储运,2011,30(01):30.[doi:10.6047/j.issn.1000-8241.2011.01.008]
　Fu Jiqiang,Wu Ming,Du Mingjun,et al.Numerical calculation on heat-moisture coupling and phase change process of the soil around buried pipeline[J].Oil & Gas Storage and Transportation,2011,30(06):30.[doi:10.6047/j.issn.1000-8241.2011.01.008]
[3]陈杨,王为民,陈伟聪,等.基于故障树与模糊理论的埋地管道风险评价[J].油气储运,2011,30(07):481.[doi:10.6047/j.issn.1000-8241.2011.07.001]
　Chen Yang,Wang Weimin,Chen Weicong,et al.Risk assessment of the buried pipeline based on the fault tree and fuzzy theory[J].Oil & Gas Storage and Transportation,2011,30(06):481.[doi:10.6047/j.issn.1000-8241.2011.07.001]
[4]胡士信,陆家榆,张本革,等.邻近强电线路管道交流干扰参数的测试方法[J].油气储运,2011,30(03):203.[doi:10.6047/j.issn.1000-8241.2011.03.012]
　Hu Shixin,Lu Jiayu,Zhang Benge,et al.Test method of AC interference parameters for pipeline adjacent power transmission lines[J].Oil & Gas Storage and Transportation,2011,30(06):203.[doi:10.6047/j.issn.1000-8241.2011.03.012]
[5]李朝阳,马贵阳,刘亮.埋地输油管道泄漏油品扩散模拟[J].油气储运,2011,30(09):674.[doi:10.6047/j.issn.1000-8241.2011.09.008]
　Li Zhaoyang,Ma Guiyang,Liu Liang,et al.Diffusion simulation on leaked oil for buried pipelines[J].Oil & Gas Storage and Transportation,2011,30(06):674.[doi:10.6047/j.issn.1000-8241.2011.09.008]
[6]王鸿膺,蒋涛,秦晓霞,等.川气东送管道土壤腐蚀埋片试验[J].油气储运,2010,29(10):769.[doi:10.6047/j.issn.1000-8241.2010.10.013]
　Wang Hongying,Jiang Tao,Qin Xiaoxia.Field Coupon Testing on the Soil Corrosivity of Sichuan-East Gas Pipeline[J].Oil & Gas Storage and Transportation,2010,29(06):769.[doi:10.6047/j.issn.1000-8241.2010.10.013]
[7]薛致远,张丰,毕武喜,等.东北管网阴极保护通电/断电电位测量与分析[J].油气储运,2010,29(10):772.[doi:10.6047/j.issn.1000-8241.2010.10.014]
　Xue Zhiyuan,Zhang Feng,Bi Wuxi.The Measurement and Analysis of On/Off-potential in Northeast Oil Pipeline Networks[J].Oil & Gas Storage and Transportation,2010,29(06):772.[doi:10.6047/j.issn.1000-8241.2010.10.014]
[8]刘仕鳌蒲红宇刘书文蒋洪.埋地管道应力分析方法[J].油气储运,2012,31(4):274.[doi:10.6047/j.issn.1000-8241.2012.04.009]
　Liu Shiao,Pu Hongyu,Liu Shuwen,et al.Stress analysis method of buried pipeline[J].Oil & Gas Storage and Transportation,2012,31(06):274.[doi:10.6047/j.issn.1000-8241.2012.04.009]
[9]吴峰,李向阳,张治军.埋地参数对管道同沟敷设传热的影响[J].油气储运,2010,29(11):835.[doi:10.6047/j.issn.1000-8241.2010.11.009]
　Wu Feng,Li Xiangyang,Zhang Zhijun.Influence of Buried Parameters for Heat Transfer of Pipelines Laid in One Ditch[J].Oil & Gas Storage and Transportation,2010,29(06):835.[doi:10.6047/j.issn.1000-8241.2010.11.009]
[10]赵雄,周卫军,郭瑞.SPAE评价法在管道防腐层性能评价中的应用[J].油气储运,2009,28(11):54.[doi:10.6047/j.issn.1000-8241.2009.11.015]
　ZHAO Xiong,ZHOU Weijun.Application of SPAE Method in Coating Performance Evaluation of Pipelines[J].Oil & Gas Storage and Transportation,2009,28(06):54.[doi:10.6047/j.issn.1000-8241.2009.11.015]

更新日期/Last Update: 2025-04-29