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《工程(英文)》 >> 2021年 第7卷 第6期 doi: 10.1016/j.eng.2020.08.026

利用机器视觉技术对化工厂管道进行自动视觉泄漏检测与定位

a Institute of Automation and Information Systems, Technical University of Munich, Garching 85748, Germany
b Evonik Technology and Infrastructure GmbH, Hanau 63450, Germany

收稿日期: 2019-12-23 修回日期: 2020-04-21 录用日期: 2020-08-05 发布日期: 2021-04-30

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摘要

在大型化工厂中,输送液体的管道的泄漏是一个重要的问题。管道的破损不仅会影响工厂的正常运行,同时也增加了维护成本。此外,还会使操作人员的生命安全受到威胁。因此,管道泄漏的检测与定位是维护和状态监测中的关键任务。近年来,大型工厂利用红外(IR)相机进行泄漏检测。红外相机可捕捉温度比周围环境温度高(或低)的液体泄漏。本文针对化工厂中的管道泄漏,提出了一种基于红外视频数据和机器视觉技术的检测与定位方法。由于所提出的方法是以视觉技术为基础,无需考虑泄漏液体的物理性质,因此其适用于任何类型的液体(水、油等)泄漏检测。在本方法中,首先对后续帧进行减影和分块处理,然后对每一分块进行主成分分析,提取特征;接着将分块内所有减影帧都转换为特征向量(作为块分类的依据),根据特征向量,采用k-最近邻算法将块分为正常(无泄漏)和异常(泄漏)两类;最后在各异常块上确定泄漏的位置。本文使用了两种不同格式的数据集(由红外相机拍摄的实验室工厂演示装置的视频图像组成)对上述方法进行评估。结果表明,本文提出的利用红外视频进行管道泄漏检测与定位的方法前景可观,具有较高的检测精度以及合理的检测时间。本文最后讨论了该方法在工厂进行实际推广的可能性及局限性。

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参考文献

[ 1 ] Patterson LA, Konschnik KE, Wiseman H, Fargione J, Maloney KO, Kiesecker J, et al. Unconventional oil and gas spills: risks, mitigation priorities, and state reporting requirements. Environ Sci Technol 2017;51(5):2563–73. 链接1

[ 2 ] Si H, Ji H, Zeng X. Quantitative risk assessment model of hazardous chemicals leakage and application. Saf Sci 2012;50(7):1452–61. 链接1

[ 3 ] Scott SL, Barrufet MA. Worldwide assessment of industry leak detection capabilities for single and multiphase pipelines. Report. College Station: Offshore Technology Research Center; 2003. 链接1

[ 4 ] Chen X, Wu Z, Chen W, Kang R, Wang S, Sang H, et al. A methodology for overall consequence assessment in oil and gas pipeline industry. Process Saf Prog 2019;38(3):e12050. 链接1

[ 5 ] Barz T, Bonow G, Hegenberg J, Habib K, Gramar L, Welle J, et al. Unmanned inspection of large industrial environments. In: Aschenbruck N, Martini P, Meier M, Tölle J, editors. Future security. Heidelberg: Springer; 2012. 链接1

[ 6 ] Datta S, Sarkar S. A review on different pipeline fault detection methods. J Loss Prev Process Ind 2016;41:97–106. 链接1

[ 7 ] Sun L, Chang N. Integrated-signal-based leak location method for liquid pipelines. J Loss Prev Process Ind 2014;32:311–8. 链接1

[ 8 ] Aamo OM. Leak detection, size estimation and localization in pipe flows. IEEE Trans Autom Control 2016;61(1):246–51. 链接1

[ 9 ] Liu C, Li Y, Xu M. An integrated detection and location model for leakages in liquid pipelines. J Pet Sci Eng 2019;175:852–67. 链接1

[10] Abhulimen KE, Susu AA. Liquid pipeline leak detection system: model development and numerical simulation. Chem Eng J 2004;97(1):47–67. 链接1

[11] Verde C. Accommodation of multi-leak location in a pipeline. Control Eng Pract 2005;13(8):1071–8. 链接1

[12] Magnis L, Petit N. Impact of measurement dating inaccuracies in the monitoring of bulk flows. Adv Electr Electron Eng 2015;13(1):30–8. 链接1

[13] Leo Kumar SP. State of the art-intense review on artificial intelligence systems application in process planning and manufacturing. Eng Appl Artif Intell 2017;65:294–329. 链接1

[14] Golnabi H, Asadpour A. Design and application of industrial machine vision systems. Robot Comput Integr Manuf 2007;23(6):630–7. 链接1

[15] Kurada S, Bradley C. A review of machine vision sensors for tool condition monitoring. Comput Ind 1997;34(1):55–72. 链接1

[16] Li R, Huang H, Xin K, Tao T. A review of methods for burst/leakage detection and location in water distribution systems. Water Sci Technol Water Supply 2015;15(3):429–41. 链接1

[17] Zhu P, Wen L, Bian X, Ling H, Hu Q. Vision meets drones: a challenge. 2018. arXiv:1804.07437.

[18] Nouacer R, Espinoza H, Ouhammou Y, Castineira Gonzalez R. Framework of key enabling technologies for safe and autonomous drones’ applications. In: Proceedings of the 22nd Euromicro Conference on Digital System Design; 2019 Aug 28–30; Kallithea, Greece; 2019. p. 420–27.

[19] Lamb T. Developing a safety culture for remotely piloted aircraft systems operations: to boldly go where no drone has gone before. In: Proceedings of SPE Health, Safety, Security, Environment, & Social Responsibility Conference— North America; 2017 Apr 18–20; New Orleans, LA, USA; 2017.

[20] Vollmer M, Möllmann KP. Infrared thermal imaging: fundamentals, research and applications. Weinheim: Wiley-VCH; 2018. 链接1

[21] Nof SY. Springer handbook of automation. Heidelberg: Springer-Verlag, Berlin Heidelberg; 2009. 链接1

[22] Yin S, Li X, Gao H, Kaynak O. Data-based techniques focused on modern industry: an overview. IEEE Trans Ind Electron 2015;62(1):657–67. 链接1

[23] Ostapkowicz P. Leak detection in liquid transmission pipelines using simplified pressure analysis techniques employing a minimum of standard and non-standard measuring devices. Eng Struct 2016;113:194–205. 链接1

[24] He G, Liang Y, Li Y, Wu M, Sun L, Xie C, et al. A method for simulating the entire leaking process and calculating the liquid leakage volume of a damaged pressurized pipeline. J Hazard Mater 2017;332:19–32. 链接1

[25] Rubinstein A, inventor; Hamut—Mechanics and Technology Compny Ltd., assignee. Fluid leakage detection system. United States patent US9939345B2. 2018 Apr 10.

[26] Ozevin D. Geometry-based spatial acoustic source location for spaced structures. Struct Health Monit 2011;10(5):503–10. 链接1

[27] Ozevin D, Harding J. Novel leak localization in pressurized pipeline networks using acoustic emission and geometric connectivity. Int J Press Vessel Pip 2012;92:63–9. 链接1

[28] Zhang H, Liang Y, Zhang W, Xu N, Guo Z, Wu G. Improved PSO-based method for leak detection and localization in liquid pipelines. IEEE Trans Ind Inform 2018;14(7):3143–54. 链接1

[29] Delgado-Aguiñaga JA, Besançon G, Begovich O, Carvajal JE. Multi-leak diagnosis in pipelines based on extended Kalman filter. Control Eng Pract 2016;49:139–48. 链接1

[30] Qu Z, Feng H, Zeng Z, Zhuge J, Jin S. A SVM-based pipeline leakage detection and pre-warning system. Measurement 2010;43(4):513–9. 链接1

[31] Da Silva HV, Morooka CK, Guilherme IR, da Fonseca TC, Mendes JRP. Leak detection in petroleum pipelines using a fuzzy system. J Pet Sci Eng 2005;49 (3–4):223–38. 链接1

[32] Wachla D, Przystalka P, Moczulski W. A method of leakage location in water distribution networks using artificial neuro-fuzzy system. IFAC-PapersOnLine 2015;48(21):1216–23. 链接1

[33] Nellis MD. Application of thermal infrared imagery to canal leakage detection. Remote Sense Environ 1982;12(3):229–34. 链接1

[34] Adefila K, Yan Y, Wang T. Leakage detection of gaseous CO2 through thermal imaging. In: Proceedings of IEEE International Instrumentation andMeasurement Technology Conference; 2015 May 11–14; Pisa, Italy; 2015. p. 261–65.

[35] Atef A, Zayed T, Hawari A, Khader M, Moselhi O. Multi-tier method using infrared photography and GPR to detect and locate water leaks. Autom Constr 2016;61:162–70. 链接1

[36] Dai D, Wang X, Zhang Y, Zhao L, Li J. Leakage region detection of gas insulated equipment by applying infrared image processing technique. In: Proceedings of the 9th International Conference on Measuring Technology and Mechatronics Automation; 2017 Jan 14–15; Changsha, China; 2017. p. 94–8.

[37] Kroll A, Baetz W, Peretzki D. On autonomous detection of pressured air and gas leaks using passive IR-thermography for mobile robot application. In: Proceedings of 2009 IEEE International Conference on Robotics and Automation; 2009 May 12–17; Kobe, Japan; 2009. p. 921–26.

[38] Wang J, Tchapmi LP, Ravikumar AP, McGuire M, Bell CS, Zimmerle D, et al. Machine vision for natural gas methane emissions detection using an infrared camera. Appl Energy 2020;257:113998. 链接1

[39] Araujo MS, Blaisdell SG, Davila DS, Dupont EM, Baldor SA, Siebenaler SP, inventors; Southwest Research Institute, assignee. Detection of hazardous leaks from pipelines using optical imaging and neural network. United States patent US20180341859. 2020 May 19.

[40] Fahimipirehgalin M, Trunzer E, Odenweller M, Vogel-Heuser B. Automatic visual leakage inspection by using thermographic video and image analysis. In: Proceedings of 2019 IEEE 15th International Conference on Automation Science and Engineering; 2019 Aug 22–26; Vancouver, BC, Canada; 2019. p. 1282–8.

[41] Partridge M, Calvo RA. Fast dimensionality reduction and simple PCA. Intell Data Anal 1998;2(1–4):203–14. 链接1

[42] Qiu B, Prinet V, Perrier E, Monga O. Multi-block PCA method for image change detection. In: Proceedings of the 12th International Conference on Image Analysis and Processing; 2003 Sep 17–19; Mantova, Italy; 2003. p. 385–90.

[43] Cunningham P, Delany SJ. k-nearest neighbour classifiers. Mult Classif Syst 2020. arXiv:2004.04523.

[44] Golub GH, Van Loan CF. Matrix computations. 4th ed. Baltimore: The Johns Hopkins University Press; 2013. 链接1

[45] Ravikumar AP, Sreedhara S, Wang J, Englander J, Roda-Stuart D, Bell C, et al. Single-blind inter-comparison of methane detection technologies—results from the Stanford/EDF mobile monitoring challenge. Elementa 2019;7:37. 链接1

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