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Wavelet-based Burst Event Detection and Localization in Water Distribution Systems

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Abstract

In this paper we present techniques for detecting and locating transient pipe burst events in water distribution systems. The proposed method uses multiscale wavelet analysis of high rate pressure data recorded to detect transient events. Both wavelet coefficients and Lipschitz exponents provide additional information about the nature of the signal feature detected and can be used for feature classification. A local search method is proposed to estimate accurately the arrival time of the pressure transient associated with a pipe burst event. We also propose a graph-based localization algorithm which uses the arrival times of the pressure transient at different measurement points within the water distribution system to determine the actual location (or source) of the pipe burst. The detection and localization performance of these algorithms is validated through leak-off experiments performed on the WaterWiSe@SG wireless sensor network test bed, deployed on the drinking water distribution system in Singapore. Based on these experiments, the average localization error is 37.5 m. We also present a systematic analysis of the sources of localization error and show that even with significant errors in wave speed estimation and time synchronization the localization error is around 56 m.

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Notes

  1. Cost figures from [2] have been adjusted for inflation.

  2. During the initial phase of the WaterWiSe@SG deployment, pressure was sampled at 2 kHz. Wavelet analysis of the burst transients revealed that the detail coefficients above 125 Hz did not aid in identifying the burst events. Thus, sampling frequency for pressure was reduced to 250 Hz.

References

  1. Comparison of European water and wastewater prices (2006). VEWA Survey, Compiled by Metropolitan Consulting Group.

  2. Distribution system inventory, integrity and water quality (2007). White Paper, U.S. Environmental Protection Agency, Prepared by American Water Works Association.

  3. Allen, M., Preis, A., Iqbal, M., Srirangarajan, S., Lim, H. B., Girod, L., Whittle, A.J. (2011). Real-time in-network distribution system monitoring to improve operational efficiency. Journal American Water Works Association (AWWA), 103(7), 63–75.

    Google Scholar 

  4. Basseville, M., & Nikiforov, I.V. (1993). Detection of abrupt changes: Theory and applications. Englewood Cliffs, New Jersey, USA: Prentice-Hall.

    Google Scholar 

  5. Berardi, L., Kapelan, Z., Giustolisi, O., Savic, D.A. (2008) Development of pipe deterioration models for water distribution systems using EPR. Journal of Hydroinformatics, 10(2), 113–126.

    Article  Google Scholar 

  6. Bergant, A., Tusseling, A.S., Vitkovsky, J.P., Covas, D.I.C., Simpson, A.R., Lambert, M.F. (2008). Parameters affecting water-hammer wave attenuation, shape and timing – Part 1: mathematical tools. Journal of Hydraulic Research, 46(3), 373–381.

    Article  Google Scholar 

  7. Bergant, A., Tusseling, A.S., Vitkovsky, J.P., Covas, D.I.C., Simpson, A.R., Lambert, M.F. (2008). Parameters affecting water-hammer wave attenuation, shape and timing – Part 2: Case studies. Journal of Hydraulic Research, 46(3), 382–391

    Article  Google Scholar 

  8. Brennan, M., Joseph, P., Muggleton, J., Gao, Y. (2006). The use of acoustic methods to detect water leaks in buried water pipes. Water and Sewerage Journal, 1, 11–14.

    Google Scholar 

  9. Covas, D., Ramos, H., Lopes, N., Almeida, A.B. (2006). Water pipe system diagnosis by transient pressure signals. In: Proc. 8th Water distribution systems analysis symposium.

  10. Dijkstra, E.W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1, 269–271. http://www-m3.ma.tum.de/twiki/pub/MN0506/WebHome/dijkstra.pdf.

    Article  MathSciNet  MATH  Google Scholar 

  11. Gao, R.X., & Yan, R. (2010). Wavelets: Theory and applications for manufacturing. Springer.

  12. Guralnik, V., & Srivastava, J. (1999). Event detection from time series data. In: Proc. 5th ACM SIGKDD Int. Conf. knowledge discovery and data mining (pp. 33–42).

  13. Hu, X.G., Ji, Y.C., Yu, W.B. (2001). The application of wavelet singularity detection in fault diagnosis of high voltage breakers. In: 27th Annual Conf. of the IEEE industrial electronics society (IECON ’01) (vol. 1, pp. 490–494).

  14. Hunaidi, O., Wang, A., Bracken, M., Gambino, T., Fricke, C. (2004). Acoustic methods for locating leaks in municipal water pipe networks. In: Int. Conf. on water demand management (pp. 1–14).

  15. Jung, B.S., Boulos, P.F., Wood, D.J. (2007). Pitfalls of water distribution model skeletonization for surge analysis. Journal American Water Works Association (AWWA), 99(12), 87–98.

    Google Scholar 

  16. Karrakchou, M., & Kunt, M. (1995). Multiscale analysis for singularity detection in pulmonary microvascular pressure transients. Annals of Biomedical Engineering 23, 562–573.

    Article  Google Scholar 

  17. Loth, J.L., Morris, G.J., Palmer, G.M., Guiler, R., Browning, P. (2004). Acoustic detecting and locating gas pipe line infringement. Final Contract Report, National Energy Technology Laboratory Strategic Center For Natural Gas.

  18. Mallat, S., & Hwang, W.L. (1992). Singularity detection and processing with wavelets. IEEE Transactions on Information Theory, 38(2), 617–643.

    Article  MathSciNet  MATH  Google Scholar 

  19. Mekle, R., Laine, A.F., Perera, G.M., DeLaPaz, R. (2000). Activation detection in fMRI data via multiscale singularity detection. In: Proc. SPIE (vol. 4119, pp. 615–625).

  20. Misiunas, D. (2005). Failure monitoring and asset condition assessment in water supply systems. Phd thesis, Lund University.

  21. Misiunas, D., Lambert, M., Simpson, A., Olsson, G. (2005). Burst detection and location in water distribution networks. Water Science and Technology: Water Supply, 5(3–4), 71–80.

    Google Scholar 

  22. Misiunas, D., Vitkovskyt’, J.P., Olsson, G., Simpson, A.R., Lambert, M.F. (2003). Pipeline burst detection and location using a continuous monitoring technique. In: Advances in Water Supply Management: Int. Conf. on Computing and Control for the Water Industry (CCWI) (pp. 89–96).

  23. Pearson, D., Fantozzi, M., Soares, D., Waldron, T. (2005). Searching for N2: How does pressure reduction reduce burst frequency? In: Proc. IWA Leakage Conf., September 2005.

  24. Silva, R.A., Buiatti, C.M., Cruz, S.L., Pereira, J.A.F. (1996). Pressure wave behaviour and leak detection in pipelines. Computers and Chemical Engineering, 20(Supplement 1), S491–S496.

    Article  Google Scholar 

  25. Srirangarajan, S., Allen, M., Preis, A., Iqbal, M., Lim, H.B., Whittle, A.J. (2010). Water main burst event detection and localization. In: Proc. 12th Water Distribution Systems Analysis Conference (WDSA).

  26. Stoianov, I., Karney, B., Covas, D., Maksimovic, C., Graham, N. (2001). Wavelet processing of transient signals for pipeline leak location and quantification. In: Int. conf. on computing and control for the water industry (CCWI) (pp. 65–76).

  27. Stoianov, I., Nachman, L., Madden, S., Tokmouline, T. (2007). Pipenet: A wireless sensor network for pipeline monitoring. In: Proc. 6th Int. conf. on information processing in sensor networks (IPSN) (pp. 264–273). ACM/IEEE.

  28. Stoianov, I., Nachman, L., Whittle, A., Madden, S., Kling, R. (2006). Sensor network for monitoring water supply and sewer systems: Lessons from Boston. In: Proc. water distribution systems analysis symp. (WDSA), (pp. 1–17).

  29. Tullis, J.P. (1989). Hydraulics of pipelines: Pumps, valves, cavitation, transients. New York: Wiley-Interscience.

    Book  Google Scholar 

  30. Wang, X.J., Lambert, M.F., Simpson, A.R., Liggett, J.A., Vitkovsky, J.P. (2002). Leak detection in pipelines using the damping of fluid transients. Journal of Hydraulic Engineering, 128(7), 697–711.

    Article  Google Scholar 

  31. Whittle, A.J., Girod, L., Preis, A., Allen, M., Lim, H.B., Iqbal, M., Srirangarajan, S., Fu, C., Wong, K.J., Goldsmith, D. (2010). WaterWiSe@SG: A testbed for continuous monitoring of the water distribution system in Singapore. In: Proc. 12th Water Distribution Systems Analysis Conference (WDSA).

  32. Zhang, H., Thurber, C.H., Rowe, C. (2003). Automatic P-wave arrival detection and picking with multiscale wavelet analysis for single-component recordings. Bulletin of the Seismological Society of America, 93(5), 1904–1912.

    Article  Google Scholar 

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Acknowledgements

This work is a collaboration between the Center for Environmental Sensing and Modeling (CENSAM) – Singapore-MIT Alliance for Research and Technology (SMART), Intelligent Systems Center (IntelliSys) at Nanyang Technological University (NTU) and Singapore Public Utilities Board (PUB). This research is supported by the Singapore National Research Foundation (NRF) through the Singapore-MIT Alliance for Research and Technology (SMART) Center for Environmental Sensing and Modeling (CENSAM). We would like to acknowledge our colleagues Cheng Fu, Lewis Girod and Kai-Juan Wong for their contributions.

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  1. Seshan Srirangarajan

    Present address: Nimbus Center, Cork Institute of Technology, Cork, Ireland

Authors and Affiliations

  1. Intelligent Systems Center, Nanyang Technological University, Singapore, 637553, Singapore

    Seshan Srirangarajan & Hock Beng Lim

  2. Singapore-MIT Alliance for Research and Technology, Singapore, 138602, Singapore

    Michael Allen, Ami Preis & Mudasser Iqbal

  3. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139-4307, USA

    Andrew J. Whittle

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  1. Seshan Srirangarajan
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Correspondence to Seshan Srirangarajan.

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Srirangarajan, S., Allen, M., Preis, A. et al. Wavelet-based Burst Event Detection and Localization in Water Distribution Systems. J Sign Process Syst 72, 1–16 (2013). https://doi.org/10.1007/s11265-012-0690-6

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