Metal magnetic memory quantitative inversion model based on IPSO-GRU algorithm for detecting submarine pipeline defect
-
摘要: 針對海底管道缺陷磁記憶定量反演的難題，提出一種基于改進粒子群優化的門控循環神經網絡模型，即IPSO-GRU模型。以兩端焊有盲板的X52管道作為實驗材料，其上預制有不同直徑、深度的缺陷，采用TSC-5M-32磁記憶檢測儀，外接11-6W非接觸探頭，進行水下磁記憶檢測試驗，提取不同缺陷尺寸的磁記憶信號特征值。考慮到磁記憶信號特征值隨缺陷尺寸呈復雜的非線性變化,引入門控循環神經網絡，利用其雙門結構能夠記憶缺陷處的信號特征，非線性回歸擬合能力強的特點，構建海底管道缺陷定量反演模型，進一步考慮到模型超參數選擇的隨機性，采用改進粒子群算法進行超參數尋優。驗證結果表明：該模型對缺陷深度反演平均精度達96%；對缺陷直徑反演平均精度達93%，為海底管道缺陷的磁記憶定量化識別與反演提供了新的思路和方法。Abstract: As submarine oil and gas are exploited further, the safety of submarine pipelines is receiving increasing attention. Due to the complex operating environment and harsh working environment, submarine pipelines are vulnerable to damage; this leads to accidents. Once an accident occurs in the submarine pipeline, it not only causes massive economic losses but also adversely affects marine ecology. The metal magnetic memory (MMM) technology was proposed in the 20th century to detect macro defects and hidden defects early. To overcome the difficulties of the MMM quantitative inversion of submarine pipeline defects, this study proposed a gated recurrent unit (GRU) neural network model based on improved particle swarm optimization (IPSO). The X52 pipe specimens with blind plates that were welded at both ends were used, pipes had prefabricated defects of different diameters and depths. An 11-6W noncontact probe was used for underwater testing; the host was the TSC-5M-32 MMM Instrument. After conducting simulated submarine tests to obtain the MMM signals of pipe defects, the characteristic parameters of MMM signals with different defect sizes were extracted. It is found that the MMM characteristic parameters exhibit a complex nonlinear variation for different defect dimensions. Exploiting the GRU’s dual-gate structure that can remember the signal characteristics of defects and its superior nonlinear regression fitting ability, a quantitative MMM GRU inversion model was established for detecting submarine pipeline defects. Furthermore, considering the randomness of the hyper-parameter selection in the model, the IPSO algorithm was used to optimize the hyper-parameters. Validation results show that the model has an average accuracy of up to 96% and 93% for defect depth inversion and defect diameter inversion, respectively. Using the MMM method, this study provides a new idea and method for the quantitative identification and defect inversion of submarine pipeline defects.
-
圖 1 GRU結構圖。（a）GRU神經網絡結構；（b）GRU單元結構
Figure 1. GRU structure: (a) illustration of GRU neural network structure; (b) illustration of GRU cell
圖 3 實驗設備。（a）試驗管道；（b）實驗流程圖
Figure 3. Experimental equipment: (a) pipe specimen; (b) experimental flowchart
圖 4 ΔHp與不同缺陷尺寸的關系。（a）缺陷直徑；（b）缺陷深度
Figure 4. Relationship between ΔHp and defect dimensions: (a) ΔHp vs defect diameter; (b) ΔHp vs defect depth
圖 5 ΔHp/Δx與不同缺陷尺寸的關系。（a）缺陷直徑；（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
圖 6 Ka與缺陷尺寸的關系。（a）Ka與缺陷直徑的關系；（b）Ka與缺陷深度的關系
Figure 6. Relationship between Ka and defect dimensions: (a) Ka vs defect diameter；(b) Ka vs defect depth
圖 7 IPSO尋優圖。（a）PSO與IPSO尋優能力對比圖；（b）IPSO適應度迭代曲線；（c）第一隱含層節點數；（d）第二隱含層節點；（e）學習率
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
圖 8 模型反演相對誤差。（a）缺陷深度相對誤差；（b）缺陷直徑相對誤差
Figure 8. Relative error graph of the model inversion: (a) defect depth; (b) defect diameter
表 1 部分原始數據
Table 1. Partial raw data
Data number Defect diameter/mm Defect depth/mm ΔHp/(A?m?1) （ΔHp/Δx）/(A?m?1?mm?1) Ka/(A?m?1?mm?1) Pipe pressure /MPa x1 defect free defect free 5.89 4.25 0.87 0 x2 defect free defect free 6.36 3.87 1.62 8 x3 10 2 16.21 4.13 24.16 0 x4 10 2 36.33 6.55 48.42 8 x5 10 3 18.45 6.24 33.17 0 x6 10 3 32.16 12.67 52.94 8 x7 10 4 26.06 6.55 39.62 0 x8 10 4 47.81 8.63 56.56 8 x9 5 4 13.77 5.29 21.91 0 x10 5 4 23.15 10.93 38.76 8 x11 15 4 35.85 8.72 62.5 0 x12 15 4 78.63 17.66 100.31 8 
下載: 導出CSV
www.77susu.com<span id="fpn9h"><noframes id="fpn9h"><span id="fpn9h"></span> <span id="fpn9h"><noframes id="fpn9h"> <th id="fpn9h"></th> <strike id="fpn9h"><noframes id="fpn9h"><strike id="fpn9h"></strike> <th id="fpn9h"><noframes id="fpn9h"> <span id="fpn9h"><video id="fpn9h"></video></span> <ruby id="fpn9h"></ruby> <strike id="fpn9h"><noframes id="fpn9h"><span id="fpn9h"></span> -
參考文獻
[1] Wang H H, Liu G H. Statistics and analysis of subsea pipeline accidents of CNOOC. China Offshore Oil Gas, 2017, 29(5): 157王紅紅, 劉國恒. 中國海油海底管道事故統計及分析. 中國海上油氣, 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蘇三慶, 劉馨為, 王威, 等. 金屬磁記憶檢測技術研究新進展與關鍵問題. 工程科學學報, 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閔希華, 楊理踐, 王國慶, 等. 長輸油氣管道弱磁應力內檢測技術. 機械工程學報, 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劉斌, 曹陽, 王締, 等. 基于LMTO算法磁記憶屈服信號的定量化分析. 儀器儀表學報, 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. 基于磁記憶的金屬管道損傷檢測及評估方法研究[學位論文]. 哈爾濱: 哈爾濱工業大學, 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時朋朋, 郝帥. 磁記憶檢測的力磁耦合型磁偶極子理論及解析解. 物理學報, 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包勝, 趙政燁, 金鵬飛, 等. 鐵磁性材料缺陷處的磁記憶信號特征分析. 工程力學, 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李立剛, 萬勇, 王宇, 等. 基于支持向量機和磁記憶技術的管道缺陷深度的定量化反演研究. 腐蝕與防護, 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): 268萬勇, 王宇, 楊勇, 等. 管道缺陷類型多特征量閾值識別方法. 油氣儲運, 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, 呂宏慶, 等. 基于模糊核支持向量機的管道磁記憶檢測缺陷識別. 石油學報, 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王帥, 黃海鴻, 韓剛, 等. 基于PCA與GA-BP神經網絡的磁記憶信號定量評價. 電子測量與儀器學報, 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駱正山, 趙樂新, 王小完. 基于動態貝葉斯網絡的海底管道點蝕疲勞損傷失效模型研究. 表面技術, 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駱正山, 秦越, 張新生, 等. 基于LASSO-WOA-LSSVM的海洋管線外腐蝕速率預測. 表面技術, 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, 陳思雨, 李思岐, 等. 基于粒子群最大似然估計的焊縫早期隱性損傷磁記憶精確定位模型. 工程科學學報, 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, 陳玉環, 李雪峰, 等. 基于動態免疫模糊聚類的金屬焊縫缺陷等級磁記憶識別模型. 儀器儀表學報, 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 -
點擊查看大圖
計量
- 文章訪問數: 574
- HTML全文瀏覽量: 466
- PDF下載量: 50
- 被引次數: 0
