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Application and performance evaluation of mass balance method for real-time pipe burst detection in supply pipeline

도수관로 실시간 관파손감지를 위한 물수지 분석 방법 적용 및 성능평가

  • Received : 2023.09.19
  • Accepted : 2023.11.15
  • Published : 2023.12.15

Abstract

Water utilities are making various efforts to reduce water losses from water networks, and an essential part of them is to recognize the moment when a pipe burst occurs during operation quickly. Several physics-based methods and data-driven analysis are applied using real-time flow and pressure data measured through a SCADA system or smart meters, and methodologies based on machining learning are currently widely studied. Water utilities should apply various approaches together to increase pipe burst detection. The most intuitive and explainable water balance method and its procedure were presented in this study, and the applicability and detection performance were evaluated by applying this approach to water supply pipelines. Based on these results, water utilities can establish a mass balance-based pipe burst detection system, give a guideline for installing new flow meters, and set the detection parameters with expected performance. The performance of the water balance analysis method is affected by the water network operation conditions, the characteristics of the installed flow meter, and event data, so there is a limit to the general use of the results in all sites. Therefore, water utilities should accumulate experience by applying the water balance method in more fields.

Keywords

References

  1. Abdulshaheed, A., Mustapha, F., and Ghavamian, A. (2017). A pressurebased method for monitoring leaks in a pipe distribution system: A review, Renew. Sustain. Energy Rev., 69(Mar), 902-911.
  2. Adedeji, K.B., Hamam, Y., Abe, B.T., and Abu-Mahfouz, A.M. (2017). Towards achieving a reliable leakage detection and localization algorithm for application in water piping networks: An overview, J. IEEE Access 5, 20272-20285.
  3. Aksela, K., Aksela, M., and Vahala., R. (2009). Leakage detection in a real distribution network using a SOM, Urban Water J. 6(4), 279-289.
  4. Ahn, J., and D. Jung. (2019). Hybrid statistical process control method for water distribution pipe burst detection, J. Water Resour. Plann. Manage., 145(9), 06019008.
  5. Bakker, M.,J., Vreeburg, H.G., Van De Roer, M., and Rietveld, L.C. (2014). Heuristic burst detection method using flow and pressure measurements, J. Hydroinf. 16(5), 1194-1209.
  6. Colombo, A.F., Lee, P., and Karney, B.W. (2009). A selective literature review of transient-based leak detection methods, J. Hydro-Environ. Res., 2(4), 212-227.
  7. Ghazali, M, Staszewski, W.J., Shucksmith, J.D., Boxall, J., and Beck, S. (2010). Instataneous phase and frequency for the detection of leaks and features in a pipeline system, Struct. Health Monitor., 10(4), 351-360.
  8. Hu, Z., Chen, B., Chen, W., Tan, D., and Shen, D. (2021). Review of model-based and data-driven approaches for leak detection and location in water distribution systems, J. Water Supply, 21, 3282-3306.
  9. Huang, P.N., Zhu, D., Hou, J., Chen, Y., Xiao, J., Yu, G., and Zhang, H. (2018). Real-time burst detection in district metering areas in water distribution system based on patterns of water demand with supervised learning, Water, 10(12), 1765.
  10. Ismail, M.I.M., Dziyauddin, R.A., Salleh, N.A.A., Muhammad-Sukki, F., Bani, N.A., Izhar, M.A.M., and Latiff, L.A. (2019). A review of vibration detection methods using accelerometer sensors for water pipeline leakage, J. IEEE Access, 7, 51965-51981.
  11. Loureiro, D., Amado, C., Martins, A., Vitorino, D., Mamade, A. and Coelho, S.T. (2016). Water distribution systems flow monitoring and anomalous event detection: a practical approach, Urban Water J., 13(3), 242-252.
  12. Jun, S., Jung, D., Lansey, K. (2021). Comparision of inputation methods for end-user demands in water distribution systems, J. Water Resour. Plann. Manage., 147(12), 04021080.
  13. Jung, D., Kang, D., Liu, J., and Lansey, K. (2015). Improving the rapidity of responses to pipe burst in water distribution systems: A comparison of statistical process control methods., J. Hydroinf., 17, (2), 307-328.
  14. Jung, D., and Lansey, K. (2015). Water distribution system burst detection using a nonlinear Kalman filter, J. Water Resour. Plann. Manage., 141(5) 04014070.
  15. Kang, D., and Lansey, K. (2014). Novel approach to detecting pipe bursts in water distribution networks, J. Water Resour. Plann. Manage., 140(1), 121-127.
  16. Li, R., Huang, H., Xin, K., and Tao, T. (2015). A review of methods for burst/leakage detection and location in water distribution systems, Water Sci. Technol. Water Supply., 15(3), 429-441.
  17. Ligget, J.A. and Chen, L.C. (1994). Inverse transient analysis in pipe networks for leakage detection, quantification and roughness calibration, J. Hydraulic Eng., 120(8), 934-955.
  18. Liou, C.P. (1993). Pipeline leak detection based on mass balance, Pipeline Infrastructure II, Preceeding of the International Conference, ASCE.
  19. Misiunas, D.J., Vitkovsky, G., Olsson, M., Lambert, and Simpson, A. (2006). Failure monitoring in water distribution networks, Water Sci. Technol., 53(4-5), 503-511.
  20. Mounce, S.R., Boxall, J.B., and Machell, J. (2007). An artificial neural network/fuzzy logic system for DMA flow meter data analysis providing burst identification and size estimation, In Proc., Combined Int. Conf. of Computing and Control for the Water Industry, CCWI2007 and Sustainable Urban Water Management, SUWM2007, London, UK, Taylor & Francis.
  21. Mounce, S.R., Boxall, J.B., and Machell., J. (2010). Development and verification of an online artificial intelligence system for detection of bursts and other abnormal flows, J. Water Resour. Plann. Manage., 136(3), 309-318.
  22. Mounce, S.R., Mounce R.B., and Boxall, J.B. (2011). Novelty detection for time series data analysis in water distribution systems using support vector machines, J. Hydroinf., 13(4), 672-686.
  23. Palau, C.V., Arregui, F.J., and Carlos., M. (2012). Burst detection in water networks using principal component analysis, J. Water Resour. Plann. Manage., 138(1), 47-54.
  24. Parry, B, Mactaggart, R., and Toerper C. (1992). Compensated volume balance leak detection on a batched LPG pipeline, Proc. Int. Offshore Mech. Arctic Eng. Sysp., 5, 501-507.
  25. Puust, R., Kapelan, Z., Savic, D.A., and Koppel. T. (2010). A review of methods for leakage management in pipe networks, Urban Water J., 7(1), 25-45.
  26. Romano, M., Kapelan, Z., and Savic, D.A. (2011). Real-time leak detection in water distribution systems, In Water distribution systems analysis 2010, Reston, VA, ASCE.
  27. Romano, M., Kapelan, Z., and Savic, D.A. (2014). Automated detection of pipe bursts and other events in water distribution systems, J. Water Resour. Plann. Manage., 140(4), 457-467.
  28. Sanz, G., Perez, R., Kapelan, Z., and Savic, D. (2016). Leak detection and localization through demand components calibration, J. Water Resour. Plann. Manage., 142(2), 04015057.
  29. Steffelbauer, D. B., Deuerlein, J., Gilbert, D., Abraham, E., and Piller, O. (2022). Pressure-leak duality for leak detection and localization in water distribution systems, J. Water Resour. Plann. Manage., 148(3), 1-13.
  30. Wan, X., Kuhanestani, P.K., Farmani, R., Keedwell, E. (2022). Literature review of data analytics for leak detection in water distribution networks: A focus on pressure and flow smart sensors, J. Water Resour. Plann. Manage., 148, 03122002.
  31. Wang, X., Guo, G., Liu, S., Wu, Y., Xu, X., and Smith., K. (2020). Burst detection in district metering areas using deep learning method, J. Water Resour. Plann. Manage., 146(6), 04020031.
  32. Wu, Y., Liu, S., Wu, X., Liu, Y., and Guan, Y. (2016). Burst detection in district metering areas using a data driven clustering algorithm Water Res., 100(Sep), 28-37.
  33. Wu, Y., and Liu, S. (2017). A review of data-driven approaches for burst detection in water distribution systems, Urban Water J., 14(9), 972-983.
  34. Wu, Y., Liu, S., Smith, K., and Wang, X. (2018). Using correlation between data from multiple monitoring sensors to detect bursts in water distribution systems, J. Water Resour. Plann. Manage., 144(2), 04017084.
  35. Wu, Y., and Liu., S., (2020). Burst detection by analyzing shape similarity of time series subsequences in district metering areas, J. Water Resour. Plann. Manage., 146(1), 04019068.
  36. Xu, Z., Ying, Z., Li, Y., He, B., and Chen., Y. (2020). Pressure prediction and abnormal working conditions detection of water supply network based on LSTM, Water Sci. Technol. Water Supply, 20(3), 963-974.
  37. Ye, G., and Fenner, R.A. (2011). Kalman filtering of hydraulic measurements for burst detection in water distribution systems, J. Pipeline Syst. Eng. Pract., 2(1), 14-22.
  38. Zaman, D., Tiwari, M.K., Gupta, A.K., and Sen, D.A. (2020). Review of leakage detection strategies for pressurised pipeline in steady-state, J. Eng. Fail. Anal., 109, 104264.