• 《工程索引》(EI)刊源期刊
  • 中文核心期刊(综合性理工农医类)
  • 中国科技论文统计源期刊
  • 中国科学引文数据库来源期刊

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于IPSO-GRU深度学习算法的海底管道缺陷尺寸磁记忆定量反演模型

邢海燕 王松弘泽 弋鸣 杨健平 朱孔阳 刘超

邢海燕, 王松弘泽, 弋鸣, 杨健平, 朱孔阳, 刘超. 基于IPSO-GRU深度学习算法的海底管道缺陷尺寸磁记忆定量反演模型[J]. 工程科学学报, 2022, 44(5): 911-919. doi: 10.13374/j.issn2095-9389.2020.11.06.001
引用本文: 邢海燕, 王松弘泽, 弋鸣, 杨健平, 朱孔阳, 刘超. 基于IPSO-GRU深度学习算法的海底管道缺陷尺寸磁记忆定量反演模型[J]. 工程科学学报, 2022, 44(5): 911-919. doi: 10.13374/j.issn2095-9389.2020.11.06.001
XING Hai-yan, WANG Song-hong-ze, YI Ming, YANG Jian-ping, ZHU Kong-yang, LIU Chao. Metal magnetic memory quantitative inversion model based on IPSO-GRU algorithm for detecting submarine pipeline defect[J]. Chinese Journal of Engineering, 2022, 44(5): 911-919. doi: 10.13374/j.issn2095-9389.2020.11.06.001
Citation: XING Hai-yan, WANG Song-hong-ze, YI Ming, YANG Jian-ping, ZHU Kong-yang, LIU Chao. Metal magnetic memory quantitative inversion model based on IPSO-GRU algorithm for detecting submarine pipeline defect[J]. Chinese Journal of Engineering, 2022, 44(5): 911-919. doi: 10.13374/j.issn2095-9389.2020.11.06.001

基于IPSO-GRU深度学习算法的海底管道缺陷尺寸磁记忆定量反演模型

doi: 10.13374/j.issn2095-9389.2020.11.06.001
基金项目: 黑龙江省自然科学基金联合引导资助项目(LH2019A004);国家自然科学基金资助项目(11272084)
详细信息
    通讯作者:

    E-mail: xxhhyyhit@163.com

  • 中图分类号: TG441.7

Metal magnetic memory quantitative inversion model based on IPSO-GRU algorithm for detecting submarine pipeline defect

More Information
  • 摘要: 针对海底管道缺陷磁记忆定量反演的难题,提出一种基于改进粒子群优化的门控循环神经网络模型,即IPSO-GRU模型。以两端焊有盲板的X52管道作为实验材料,其上预制有不同直径、深度的缺陷,采用TSC-5M-32磁记忆检测仪,外接11-6W非接触探头,进行水下磁记忆检测试验,提取不同缺陷尺寸的磁记忆信号特征值。考虑到磁记忆信号特征值随缺陷尺寸呈复杂的非线性变化,引入门控循环神经网络,利用其双门结构能够记忆缺陷处的信号特征,非线性回归拟合能力强的特点,构建海底管道缺陷定量反演模型,进一步考虑到模型超参数选择的随机性,采用改进粒子群算法进行超参数寻优。验证结果表明:该模型对缺陷深度反演平均精度达96%;对缺陷直径反演平均精度达93%,为海底管道缺陷的磁记忆定量化识别与反演提供了新的思路和方法。

     

  • 图  1  GRU结构图。(a)GRU神经网络结构;(b)GRU单元结构

    Figure  1.  GRU structure: (a) illustration of GRU neural network structure; (b) illustration of GRU cell

    图  2  IPSO优化超参数流程图

    Figure  2.  IPSO optimized hyper-parameter flowchart

    图  3  实验设备。(a)试验管道;(b)实验流程图

    Figure  3.  Experimental equipment: (a) pipe specimen; (b) experimental flowchart

    图  4  ΔHp与不同缺陷尺寸的关系。(a)缺陷直径;(b)缺陷深度

    Figure  4.  Relationship between ΔHp and defect dimensions: (a) ΔHp vs defect diameter; (b) ΔHp vs defect depth

    图  5  ΔHp/Δx与不同缺陷尺寸的关系。(a)缺陷直径;(b)缺陷深度

    Figure  5.  Relationship between ΔHp/Δx and defect dimensions: (a) ΔHp/Δx vs defect dimensions diameter; (b) ΔHp/Δx vs defect dimensions depth

    图  6  Ka与缺陷尺寸的关系。(a)Ka与缺陷直径的关系;(b)Ka与缺陷深度的关系

    Figure  6.  Relationship between Ka and defect dimensions: (a) Ka vs defect diameter;(b) Ka vs defect depth

    图  7  IPSO寻优图。(a)PSO与IPSO寻优能力对比图;(b)IPSO适应度迭代曲线;(c)第一隐含层节点数;(d)第二隐含层节点;(e)学习率

    Figure  7.  IPSO optimization graph: (a) optimization capability comparison of PSO vs IPSO; (b) IPSO fitness value iteration curve; (c) first hidden nodes value; (d) second hidden nodes value; (e) learning rate

    图  8  模型反演相对误差。(a)缺陷深度相对误差;(b)缺陷直径相对误差

    Figure  8.  Relative error graph of the model inversion: (a) defect depth; (b) defect diameter

    表  1  部分原始数据

    Table  1.   Partial raw data

    Data numberDefect diameter/mmDefect depth/mmΔHp/(A∙m−1)(ΔHp/Δx)/(A∙m−1∙mm−1)Ka/(A∙m−1∙mm−1)Pipe pressure /MPa
    x1defect freedefect free5.894.250.870
    x2defect freedefect free6.363.871.628
    x310216.214.1324.160
    x410236.336.5548.428
    x510318.456.2433.170
    x610332.1612.6752.948
    x710426.066.5539.620
    x810447.818.6356.568
    x95413.775.2921.910
    x105423.1510.9338.768
    x1115435.858.7262.50
    x1215478.6317.66100.318
    下载: 导出CSV
  • [1] Wang H H, Liu G H. Statistics and analysis of subsea pipeline accidents of CNOOC. China Offshore Oil Gas, 2017, 29(5): 157

    王红红, 刘国恒. 中国海油海底管道事故统计及分析. 中国海上油气, 2017, 29(5):157
    [2] Dubov A A. A study of metal properties using the method of magnetic memory. Met Sci Heat Treat, 1997, 39(9): 401 doi: 10.1007/BF02469065
    [3] Su S Q, Liu X W, Wang W, et al. Progress and key problems in the research on metal magnetic memory testing technology. Chin J Eng, 2020, 42(12): 1557

    苏三庆, 刘馨为, 王威, 等. 金属磁记忆检测技术研究新进展与关键问题. 工程科学学报, 2020, 42(12):1557
    [4] Min X H, Yang L J, Wang G Q, et al. Weak magnetism stress internal testing technology of the long distance oil-gas pipeline. J Mech Eng, 2017, 53(12): 19 doi: 10.3901/JME.2017.12.019

    闵希华, 杨理践, 王国庆, 等. 长输油气管道弱磁应力内检测技术. 机械工程学报, 2017, 53(12):19 doi: 10.3901/JME.2017.12.019
    [5] Liu B, Cao Y, Wang D, et al. Quantitative analysis of the magnetic memory yielding signal characteristics based on the LMTO algorithm. Chin J Sci Instrum, 2017, 38(6): 1413 doi: 10.3969/j.issn.0254-3087.2017.06.012

    刘斌, 曹阳, 王缔, 等. 基于LMTO算法磁记忆屈服信号的定量化分析. 仪器仪表学报, 2017, 38(6):1413 doi: 10.3969/j.issn.0254-3087.2017.06.012
    [6] Venkatachalapathi N, basha S M J, Raju G J, et al. Characterization of fatigued steel states with metal magnetic memory method. Mater Today:Proc, 2018, 5(2): 8645 doi: 10.1016/j.matpr.2018.04.002
    [7] Jiao J N. Damage Testing of the Metal Pipeline Based on Metal Magnetic Memory and Research on Evaluation Methed [Dissertation]. Harbin: Harbin Institute of Technology, 2015

    焦江娜. 基于磁记忆的金属管道损伤检测及评估方法研究[学位论文]. 哈尔滨: 哈尔滨工业大学, 2015
    [8] Shi P P, Hao S. Analytical solution of magneto-mechanical magnetic dipole model for metal magnetic memory method. Acta Phys Sin, 2021, 70(3): 105

    时朋朋, 郝帅. 磁记忆检测的力磁耦合型磁偶极子理论及解析解. 物理学报, 2021, 70(3):105
    [9] Bao S, Zhao Z Y, Jin P F, et al. Study on the characteristics of magnetic memory signals at ferromagnetic material defects. Eng Mech, 2020, 37(Sup 1): 371

    包胜, 赵政烨, 金鹏飞, 等. 铁磁性材料缺陷处的磁记忆信号特征分析. 工程力学, 2020, 37(增刊1): 371
    [10] Xu K S, Yang K, Liu J, et al. Study on metal magnetic memory signal of buried defect in fracture process. J Magn Magn Mater, 2020, 498: 166139 doi: 10.1016/j.jmmm.2019.166139
    [11] Xu K S, Feng M G, Qiu X X. Distinguishing welding defects from the stress concentration zone using metal magnetic memory field parameters. Trans Indian Inst Met, 2019, 72(2): 343 doi: 10.1007/s12666-018-1485-7
    [12] Xu K S, Liu J W, Yang K, et al. Effect of applied load and thermal treatment on the magnetic memory signal of defect-bearing Q345R steel samples. J Magn Magn Mater, 2021, 539: 168366 doi: 10.1016/j.jmmm.2021.168366
    [13] Li L G, Wan Y, Wang Y, et al. Quantitative inversion of pipeline defect depth based on support vector machine and magnetic memory technology. Corros Prot, 2020, 41(1): 29 doi: 10.11973/fsyfh-202001006

    李立刚, 万勇, 王宇, 等. 基于支持向量机和磁记忆技术的管道缺陷深度的定量化反演研究. 腐蚀与防护, 2020, 41(1):29 doi: 10.11973/fsyfh-202001006
    [14] Wan Y, Wang Y, Yang Y, et al. Multi-characteristic threshold identification method for pipeline defect types. Oil Gas Storage Transp, 2020, 39(3): 268

    万勇, 王宇, 杨勇, 等. 管道缺陷类型多特征量阈值识别方法. 油气储运, 2020, 39(3):268
    [15] Yi F, Li Z X, Lü H Q, et al. Defect recognition by metal magnetic memory detection of pipelines based on the fuzzy kernel function SVM. Acta Petrolei Sin, 2010, 31(5): 863 doi: 10.7623/syxb201005030

    易方, 李著信, 吕宏庆, 等. 基于模糊核支持向量机的管道磁记忆检测缺陷识别. 石油学报, 2010, 31(5):863 doi: 10.7623/syxb201005030
    [16] El-Abbasy M S, Senouci A, Zayed T, et al. A condition assessment model for oil and gas pipelines using integrated simulation and analytic network process. Struct Infrastructure Eng, 2015, 11(3): 263 doi: 10.1080/15732479.2013.873471
    [17] Wang S, Huang H H, Han G, et al. Quantitative evaluation of magnetic memory signal based on PCA & GA-BP neural network. J Electron Meas Instrum, 2018, 32(10): 190

    王帅, 黄海鸿, 韩刚, 等. 基于PCA与GA-BP神经网络的磁记忆信号定量评价. 电子测量与仪器学报, 2018, 32(10):190
    [18] Luo Z S, Zhao L X, Wang X W. Failure model for pitting fatigue damaged pipeline of subsea based on dynamic Bayesian network. Surf Technol, 2020, 49(1): 269

    骆正山, 赵乐新, 王小完. 基于动态贝叶斯网络的海底管道点蚀疲劳损伤失效模型研究. 表面技术, 2020, 49(1):269
    [19] Luo Z S, Qin Y, Zhang X S, et al. Prediction of external corrosion rate of marine pipelines based on LASSO-WOA-LSSVM. Surface Technology, 2021(5): 245

    骆正山, 秦越, 张新生, 等. 基于LASSO-WOA-LSSVM的海洋管线外腐蚀速率预测. 表面技术, 2021(5):245
    [20] Xing H Y, Chen S Y, Li S Q, et al. MMM accurate location model of early hidden damage in welded joints based on PSO and MLE. Chin J Eng, 2017, 39(10): 1559

    邢海燕, 陈思雨, 李思岐, 等. 基于粒子群最大似然估计的焊缝早期隐性损伤磁记忆精确定位模型. 工程科学学报, 2017, 39(10):1559
    [21] Xing H Y, Chen Y H, Li X F, et al. Magnetic memory identification model of mental weld defect levels based on dynamic immune fuzzy clustering. Chin J Sci Instrum, 2019, 40(11): 225

    邢海燕, 陈玉环, 李雪峰, 等. 基于动态免疫模糊聚类的金属焊缝缺陷等级磁记忆识别模型. 仪器仪表学报, 2019, 40(11):225
    [22] Zhou W, Fan J C, Liu X Y, et al. Metal magnetic memory testing of the drilling riser pipeline steel based on pulsating-impact-fatigue test. Mater Sci Forum, 2020, 975: 15 doi: 10.4028/www.scientific.net/MSF.975.15
    [23] Zhao B X, Yao K, Wu L B, et al. Application of metal magnetic memory testing technology to the detection of stress corrosion defect. Appl Sci, 2020, 10(20): 7083 doi: 10.3390/app10207083
    [24] Cho K, Merrirnboer B V, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[J/OL]. arXiv. org online (2014-6-3) [2021-5-1]. https://arxiv.org/abs/1406.1078
    [25] Kennedy J, Eberhart R. Particle swarm optimization // Proceedings of ICNN'95-IEEE International Conference on Neural Networks. Perth, 1995: 1942
  • 加载中
图(8) / 表(1)
计量
  • 文章访问数:  154
  • HTML全文浏览量:  198
  • PDF下载量:  24
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-11-06
  • 网络出版日期:  2021-09-29
  • 刊出日期:  2022-05-05

目录

    /

    返回文章
    返回