TDR-based Multiple Leak Detection System using an S-parameter Transmission Line Model for Long-Distance Pipelines
Leaks in water distribution systems should be detected to avoid economic, environmental, and social problems. Existing Bayesian Inference based time-domainreflectometry (TDR) methods for leak detection have a limitation for real applications due to the lengthy time in building sample data. As the pipeline distance becomes longer and multiple leaks must be considered in long distance pipelines, the computational time for building training data gets larger. This paper proposes a scattering-parameter-based forward model to relieve computational burden of the existing TDR methods. It was shown that the proposed model outperformed the existing RLGC-based forward model in terms of computational time. The proposed model that is combined with Bayesian inference and TDR signal modeling is validated with an experimental pipeline, leak detectors, transmission line, and TDR instrument for leak detection. In summary, the proposed method is promising for leak detection in long pipelines as well as multiple leaks.
Pipe leak detection, time-domain reflectometry, S-parameter model
Cheong, L. C.. (1991). Unaccounted for water and the economics of leak detection. Proc. International Water Supply Congress and Exhibition, Copenhagen.
Thornton, J., Sturm, R., & Kunkel, G. (2008). Water loss control : McGraw Hill Professional.
Moe, C. L., & Rheingans, R. D. (2006). Global challenges in water, sanitation and health. Journal of water and health, vol. 4, p. 41.
CNT(The Center for Neighborhood Technology) (2013). The Case for fixing the Leaks: Protecting people and saving while supporting economic growth in the Great Lakes region. http://www.cnt.org/publications/the-casefor-fixing-the-leaks-protecting-people-and-savingwater-while-supporting.
Gao, Y., Brennan, M., Joseph, P., Muggleton, J., & Hunaidi, O. (2004). A model of the correlation function of leak noise in buried plastic pipes. Journal of Sound and Vibration, vol. 277, pp. 133-148.
O'Brien, E., Murray, T., & McDonald, A. (2003). Detecting leaks from water pipes at a test facility using groundpenetrating radar. Pumps, Electromechanical Devices and Systems Applied to Urban Water Management, vol. 1, p. 395.
Demirci, S., Yigit, E., Eskidemir, I. H., & Ozdemir, C. (2012). Ground penetrating radar imaging of water leaks from buried pipes based on back- projection method. NDT & E International, vol. 47, pp. 35-42.
Costello, S., Chapman, D., Rogers, C., & Metje, N. (2007). Underground asset location and condition assessment technologies. Tunnelling and Underground Space Technology, vol. 22, pp. 524- 542.
McNulty, J. (2001). An acoustic-based system for detecting, locating and sizing leaks in water pipelines. Proceedings of the 4th International Conference on Water Pipeline Systems: Managing Pipeline Assets in an Evolving Market. York, UK.
Vítkovský, J. P., Lambert, M. F., Simpson, A. R., & Liggett, J. A. (2007). Experimental observation and analysis of inverse transients for pipeline leak detection. Journal of Water Resources Planning and Management, vol. 133, pp. 519-530.
Ghazali, M., Staszewski, W. J., Shucksmith, J., Boxall, J. B., & Beck, S. B. (2010). Instantaneous phase and frequency for the detection of leaks and features in a pipeline system. Structural Health Monitoring.
Covas, D., Ramos, H., & De Almeida, A. B. (2005). Standing wave difference method for leak detection in pipeline systems. Journal of Hydraulic Engineering, vol. 131, pp. 1106-1116.
Brunone, B. (1999). Transient test-based technique for leak detection in outfall pipes. Journal of water resources planning and management, vol. 125, pp. 302-306.
Puust, R., Kapelan, Z., Savic, D., & Koppel, T. (2010). A review of methods for leakage management in pipe networks. Urban Water Journal, vol. 7, pp. 25-45.
Kim, Y., Suh, J., Cho, J., Singh, S., & Seo, J. (2015). Development of Real-Time Pipeline Management System for Prevention of Accidents. International Journal of Control and Automation, vol. 8, pp. 211- 226.
Cataldo, A., Cannazza, G., De Benedetto, E., & Giaquinto, N. (2012). A TDR-based system for the localization of leaks in newly installed, underground pipes made of any material. Measurement Science & Technology, vol. 23, p. 9.
Cataldo, A., Cannazza, G., De Benedetto, E., & Giaquinto, N. (2012). A new method for detecting leaks in underground water pipelines. Sensors Journal, IEEE, vol. 12, pp. 1660-1667.
Cataldo, A., Cannazza, G., De Benedetto, E., & Giaquinto, N. (2012). Experimental validation of a TDR-based system for measuring leak distances in buried metal pipes. Progress in Electromagnetics Research- Pier, vol. 132, pp. 71-90.
Kim, T., Woo, S., Youn , B. & Huh, Y. (2015), TDR-based Pipe Leakage Detection and Location using Bayesian Inference. Prognostics and Health Management (PHM), 2015 IEEE Conference on, pp. 1-5.
O'Connor, K. M. & Dowding, C. H. (1999), Geomeasurements by pulsing TDR cables and probes: CRC Press.
Yang, X., Choi, M.-S., Lee, S.-J., Ten, C.-W., & Lim, S. -I. (2008). Fault location for underground power cable using distributed parameter approach. Power Systems, IEEE Transactions on, vol. 23, pp. 1809- 1816.
Kwon, D., Azarian, M. H., & Pecht, M. (2009). Early Detection of Interconnect Degradation by Continuous Monitoring of RF Impedance. Ieee Transactions on Device and Materials Reliability, vol. 9, pp. 296-304.
Yu, X., Zhang, B., Tao, J., & Yu, X. (2013). A new timedomain reflectometry bridge scour sensor. Structural Health Monitoring, vol. 12, pp. 99-113.
Yu, X. & Yu, X. (2009). Time domain reflectometry automatic bridge scour measurement system: principles and potentials. Structural Health Monitoring, vol. 8, pp. 463-476.
Calamita, G., Brocca, L., Perrone, A., Piscitelli, S., Lapenna, V., & Melone, F., et al (2012). Electrical resistivity and TDR methods for soil moisture estimation in central Italy test-sites. Journal of Hydrology, vol. 454, pp. 101-112.
Ledieu, J., De Ridder, P., De Clerck, P., & Dautrebande, S. (1986). A method of measuring soil moisture by timedomain reflectometry. Journal of Hydrology, vol. 88, pp. 319-328.
Di Sante, R. (2005). Time domain reflectometry-based liquid level sensor. Review of Scientific Instruments, vol. 76, p. 5.
Wang, P. F., Youn, B. D., Xi, Z. M., & Kloess, A. (2009). Bayesian Reliability Analysis With Evolving, Insufficient, and Subjective Data Sets. Journal of Mechanical Design, vol. 131.
Wang. P. F., Youn, B. D., & Hu, C. (2012). A generic probabilistic framework for structural health prognostics and uncertainty management. Mechanical Systems and Signal Processing, vol. 28, pp. 622-637.
Reinhold, L., & Pavel, B. (2000). RF circuit design: theory and applications : Prentice Hall Upper Saddle River.
Schuet, S., Timucin, D., & Wheeler, K. (2011). A Model- Based Probabilistic Inversion Framework for Characterizing Wire Fault Detection Using TDR. Ieee Transactions on Instrumentation and Measurement, vol. 60, pp. 1654-1663.
Miller, S. J. (2006). The method of least squares. Mathematics Department Brown University, pp. 1- 7.
Twisk, D., Spit, H., Beebe, M., & Depinet, P. (2007). Effect of Dummy Repeatability on Numerical Model Accuracy. SAE Technical Paper 0148-7191.
Gutiérrez, F., Galve, J., Guerrero, J., Lucha, P., Cendrero, A., & Remondo, J., et al. (2007). The origin, typology, spatial distribution and detrimental effects of the sinkholes developed in the alluvial evaporite karst of the Ebro River valley downstream of Zaragoza city (NE Spain). Earth Surface Processes and Landforms, vol. 32, pp. 912-928.
Zhang, X., Zhang, M., & Liu, D. (2013). Practicable model of coaxial cable channel with splitter and tap via statetransition matrix. Measurement, vol. 46, pp. 1190-1199.