Abstract
In recent years, due to the internal and external factors, pipeline leakage accidents happen frequently which lead to hidden danger to the safe operation of the pipeline. The pipeline leakage accidents not only cause serious economic loss, but also harm the safe operation of pipeline and personal safety. Consequently, it is extremely significant to detect and locate the leakage of the pipeline in time. Existing methods of detecting and locating pipeline leakage can be divided into two types, external and internal monitoring. This chapter summarizes the common methods for pipeline leak detection and location, including acoustic methods, negative pressure waves, intelligent algorithm-based methods, and data-driven methods. The applications of different methods are also given to compare their strengths and weaknesses.
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References
Wang B, Liang Y, Zheng T, Yuan M, Zhang H. Optimisation of a downstream oil supply chain with new pipeline route planning. Chemical Engineering Research and Design. 2019;145:300–13. https://doi.org/10.1016/j.cherd.2019.03.009.
Black P. A review of pipeline leak detection technology. Pipeline systems. 1992:287–98. https://doi.org/10.1007/978-94-017-2677-1_23.
Liao Q, Liang Y, Xu N, Zhang H, Wang J, Zhou X. An MILP approach for detailed scheduling of multi-product pipeline in pressure control model. Chemical Engineering Research and Design. 2018;136:620–37. https://doi.org/10.1016/j.cherd.2018.06.016.
Sun J, Feng X, Wang Y, Deng C, Chu KH. Pump network optimization for a cooling water system. Energy. 2014;67:506–12. https://doi.org/10.1016/j.energy.2014.01.028.
Liu P, Zhou T, Li J. Application of negative pressure wave method in nuclear pipeline leakage detection. 2011 Asia-Pacific Power and Energy Engineering Conference: IEEE; 2011. p. 1–4. https://doi.org/10.1109/APPEEC.2011.5748950.
Lu W, Liang W, Zhang L, Liu W. A novel noise reduction method applied in negative pressure wave for pipeline leakage localization. Process Safety and Environmental Protection. 2016;104:142–9. https://doi.org/10.1016/j.psep.2016.08.014.
Li J, Zheng Q, Qian Z, Yang X. A novel location algorithm for pipeline leakage based on the attenuation of negative pressure wave. Process Safety and Environmental Protection. 2019;123:309–16. https://doi.org/10.1016/j.psep.2019.01.010.
Pudar RS, Liggett JA. Leaks in pipe networks. Journal of Hydraulic Engineering. 1992;118:1031–46. https://doi.org/10.1061/(ASCE)0733-9429(1992)118:7(1031).
Vítkovský JP, Lambert MF, Simpson AR, Liggett JA. Experimental observation and analysis of inverse transients for pipeline leak detection. Journal of Water Resources Planning and Management. 2007;133:519–30. https://doi.org/10.1061/(ASCE)0733-9496(2007)133:6(519).
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 Transactions on Industrial Informatics. 2018;14:3143–54. https://doi.org/10.1109/TII.2018.2794987.
Huang Y-C, Lin C-C, Yeh H-D. An optimization approach to leak detection in pipe networks using simulated annealing. Water Resources Management. 2015;29:4185–201. https://doi.org/10.1007/s11269-015-1053-4.
Li J, Liu W, Sun Z, Cui L. A new failure detection method and its application in leak monitor of pipeline. 2008 10th International Conference on Control, Automation, Robotics and Vision: IEEE; 2008. p. 1178–82. https://doi.org/10.1109/ICARCV.2008.4795688.
Kang J, Park Y-J, Lee J, Wang S-H, Eom D-S. Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems. IEEE Transactions on Industrial Electronics. 2017;65:4279–89. https://doi.org/10.1109/TIE.2017.2764861.
Liu C, Li Y, Xu M. An integrated detection and location model for leakages in liquid pipelines. Journal of Petroleum Science and Engineering. 2019;175:852–67. https://doi.org/10.1016/j.petrol.2018.12.078.
Xu T, Chen S, Guo S, Huang X, Li J, Zeng Z. A small leakage detection approach for oil pipeline using an inner spherical ball. Process Safety and Environmental Protection. 2019;124:279–89. https://doi.org/10.1016/j.psep.2018.11.009.
Liu J, Zang D, Liu C, Ma Y, Fu M. A leak detection method for oil pipeline based on markov feature and two-stage decision scheme. Measurement. 2019;138:433–45. https://doi.org/10.1016/j.measurement.2019.01.029.
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. Advances in neural information processing systems. 2014;27.
Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. Advances in neural information processing systems. 2016;29.
Yu L, Zhang W, Wang J, Yu Y. Seqgan: Sequence generative adversarial nets with policy gradient. Proceedings of the AAAI conference on artificial intelligence 2017. https://doi.org/10.1609/aaai.v31i1.10804.
Mao X, Li Q, Xie H, Lau RY, Wang Z, Paul Smolley S. Least squares generative adversarial networks. Proceedings of the IEEE international conference on computer vision2017. p. 2794–802. https://doi.org/10.48550/arXiv.1611.04076
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Du, J., Zheng, J. (2023). Intelligent Leakage Detection for Pipelines. In: Su, H., Liao, Q., Zhang, H., Zio, E. (eds) Advanced Intelligent Pipeline Management Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-9899-7_11
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DOI: https://doi.org/10.1007/978-981-19-9899-7_11
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