State-of-the-Art Reviews
Jul 23, 2021

Review of Water Leak Detection and Localization Methods through Hydrophone Technology

Publication: Journal of Pipeline Systems Engineering and Practice
Volume 12, Issue 4

Abstract

Acoustic technologies are popular for detection of leak detriments in water pipelines. However, problems of false alarms, detection of weak or difficult leaks, accurate leak pinpointing, and the high cost of long-term monitoring remain prevalent. These issues demand a more sophisticated testing approach suitable for real-world application. In particular, hydrophone technology has strong promise for long-range leak detection in high-attenuation conditions. However, existing review studies only cover the methods of leak detection holistically, with limited insight into the practical implementation of sensing technologies for water leak detection. In particular, the problem of detecting and localizing leaks using hydroacoustic data has not yet been extensively studied. The current study, therefore, presents a state-of-the-art review of the extant literature on water leak detection and localization taking hydrophones as a good example of hydroacoustic water leak detection. The study compares hydrophones with other popular sensing technologies such as accelerometers and guides on its better application for detecting water leaks. Current research directions, gaps, and future work foci are also identified to enable further development of a hydrophone-based water leak detection system. Review shows that existing experiments are limited to controlled conditions where impacts of surrounding strata, ambient noise, and difficult pipe geometries cannot be studied. Future studies can apply the technology to real-life cases, developing faster analytical methods and hybrid solutions using a multisensing approach. This can help water leak experts enormously in cost-effective, efficient detection of leaks.

Get full access to this article

View all available purchase options and get full access to this article.

Data Availability Statement

All data supporting the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors gratefully acknowledge the support from the Innovation and Technology Fund [Innovation and Technology Support Programme (ITSP)] under Grant No. ITS/067/19FP and the Water Supplies Department of the Government of Hong Kong.

References

Almeida, F., M. Brennan, P. Joseph, S. Whitfield, S. Dray, and A. Paschoalini. 2014. “On the acoustic filtering of the pipe and sensor in a buried plastic water pipe and its effect on leak detection: An experimental investigation.” Sensors 14 (3): 5595–5610. https://doi.org/10.3390/s140305595.
Almeida, F. C., M. J. Brennan, P. F. Joseph, S. Dray, S. Whitfield, and A. T. Paschoalini. 2015. “Towards an in-situ measurement of wave velocity in buried plastic water distribution pipes for the purposes of leak location.” J. Sound Vib. 359 (Dec): 40–55. https://doi.org/10.1016/j.jsv.2015.06.015.
Almeida, F. C. L., M. J. Brennan, P. F. Joseph, Y. Gao, and A. T. Paschoalini. 2018. “The effects of resonances on time delay estimation for water leak detection in plastic pipes.” J. Sound Vib. 420 (Apr): 315–329. https://doi.org/10.1016/j.jsv.2017.06.025.
Bracken, M., and B. Cain. 2012. “Transmission main and plastic pipe leak detection using advanced correlation technology: Case studies.” In Proc., Pipelines Conf. 2012, 147–157. Reston, VA: ASCE.
Brennan, M. J., Y. Gao, P. C. Ayala, F. C. L. Almeida, P. F. Joseph, and A. T. Paschoalini. 2019. “Amplitude distortion of measured leak noise signals caused by instrumentation: Effects on leak detection in water pipes using the cross-correlation method.” J. Sound Vib. 461 (Nov): 114905. https://doi.org/10.1016/j.jsv.2019.114905.
Brennan, M. J., Y. Gao, and P. F. Joseph. 2007. “On the relationship between time and frequency domain methods in time delay estimation for leak detection in water distribution pipes.” J. Sound Vib. 304 (1–2): 213–223. https://doi.org/10.1016/j.jsv.2007.02.023.
Brunner, A. J., and M. Barbezat. 2006. “Acoustic emission monitoring of leaks in pipes for transport of liquid and gaseous media: A model experiment.” Adv. Mater. Res. 13–14: 351–356. https://doi.org/10.4028/www.scientific.net/AMR.13-14.351.
Butterfield, J. D., R. P. Collins, and S. B. Beck. 2018a. “Influence of pipe material on the transmission of vibroacoustic leak signals in real complex water distribution systems: Case study.” J. Pipeline Syst. Eng. Pract. 9 (3): 05018003. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000321.
Butterfield, J. D., A. Krynkin, R. P. Collins, and S. B. M. Beck. 2017. “Experimental investigation into vibro-acoustic emission signal processing techniques to quantify leak flow rate in plastic water distribution pipes.” Appl. Acoust. 119 (Apr): 146–155. https://doi.org/10.1016/j.apacoust.2017.01.002.
Butterfield, J. D., V. Meruane, R. P. Collins, G. Meyers, and S. B. M. Beck. 2018b. “Prediction of leak flow rate in plastic water distribution pipes using vibro-acoustic measurements.” Struct. Health Monit. 17 (4): 959–970. https://doi.org/10.1177/1475921717723881.
Casillas, M. V., V. Puig, L. E. Garza-Castanón, and A. Rosich. 2013. “Optimal sensor placement for leak location in water distribution networks using genetic algorithms.” Sensors 13 (11): 14984–15005. https://doi.org/10.3390/s131114984.
Cody, R., J. Harmouche, and S. Narasimhan. 2018. “Leak detection in water distribution pipes using singular spectrum analysis.” Urban Water J. 15 (7): 636–644. https://doi.org/10.1080/1573062X.2018.1532016.
Cody, R. A., P. Dey, and S. Narasimhan. 2020a. “Linear prediction for leak detection in water distribution networks.” J. Pipeline Syst. Eng. Pract. 11 (1): 04019043. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000415.
Cody, R. A., B. A. Tolson, and J. Orchard. 2020b. “Detecting leaks in water distribution pipes using a deep autoencoder and hydroacoustic spectrograms.” J. Comput. Civ. Eng. 34 (2): 04020001. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000881.
Datta, S., and S. Sarkar. 2016. “A review on different pipeline fault detection methods.” J. Loss Prev. Process Ind. 41 (May): 97–106. https://doi.org/10.1016/j.jlp.2016.03.010.
El-Abbasy, M. S., F. Mosleh, A. Senouci, T. Zayed, and H. Al-Derham. 2016. “Locating leaks in water mains using noise loggers.” J. Infrastruct. Syst. 22 (3): 04016012. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000305.
El-Zahab, S., and T. Zayed. 2019. “Leak detection in water distribution networks: An introductory overview.” Smart Water 4 (1): 1–23. https://doi.org/10.1186/s40713-019-0017-x.
Ferreira, F. A. 2018. “Mapping the field of arts-based management: Bibliographic coupling and co-citation analyses.” J. Bus. Res. 85 (Apr): 348–357. https://doi.org/10.1016/j.jbusres.2017.03.026.
Gao, Y., M. J. Brennan, and P. Joseph. 2004a. “Detecting leaks in buried plastic pipes using correlation techniques. Part 1: A model of the correlation function of leak noise.” In Proc., 18th Int. Congress on Acoustics. Southampton, Hampshire: Univ. of Southampton.
Gao, Y., M. J. Brennan, and P. Joseph. 2004b. “Detecting leaks in buried plastic pipes using correlation techniques. Part 2: On the selection of acoustic/vibration sensors.” In Proc., 18th Int. Congress on Acoustics. Southampton, Hampshire: Univ. of Southampton.
Gao, Y., M. J. Brennan, and P. F. Joseph. 2006. “A comparison of time delay estimators for the detection of leak noise signals in plastic water distribution pipes.” J. Sound Vib. 292 (3–5): 552–570. https://doi.org/10.1016/j.jsv.2005.08.014.
Gao, Y., M. J. Brennan, and P. F. Joseph. 2009. “On the effects of reflections on time delay estimation for leak detection in buried plastic water pipes.” J. Sound Vib. 325 (3): 649–663. https://doi.org/10.1016/j.jsv.2009.03.037.
Gao, Y., M. J. Brennan, P. F. Joseph, J. M. Muggleton, and O. Hunaidi. 2005. “On the selection of acoustic/vibration sensors for leak detection in plastic water pipes.” J. Sound Vib. 283 (3–5): 927–941. https://doi.org/10.1016/j.jsv.2004.05.004.
Gao, Y., M. J. Brennan, Y. Y. Liu, F. C. L. Almeida, and P. F. Joseph. 2017. “Improving the shape of the cross-correlation function for leak detection in a plastic water distribution pipe using acoustic signals.” Appl. Acoust. 127 (Dec): 24–33. https://doi.org/10.1016/j.apacoust.2017.05.033.
Gao, Y., and Y. Liu. 2017. “Theoretical and experimental investigation into structural and fluid motions at low frequencies in water distribution pipes.” Mech. Syst. Sig. Process. 90 (Jun): 126–140. https://doi.org/10.1016/j.ymssp.2016.12.018.
Gao, Y., Y. Liu, Y. Ma, X. Cheng, and J. Yang. 2018. “Application of the differentiation process into the correlation-based leak detection in urban pipeline networks.” Mech. Syst. Sig. Process. 112 (Nov): 251–264. https://doi.org/10.1016/j.ymssp.2018.04.036.
Guo, C., K. Shi, and X. Chu. 2019. “Experimental study on leakage monitoring of pressurized water pipeline based on fiber optic hydrophone.” Water Supply 19 (8): 2347–2358. https://doi.org/10.2166/ws.2019.116.
Hamilton, S., and B. Charalambous. 2020. Leak detection: Technology and implementation. London: IWA Publishing.
Harmouche, J., and S. Narasimhan. 2019. “Long-term monitoring for leaks in water distribution networks using association rules mining.” IEEE Trans. Ind. Inf. 16 (1): 258–266. https://doi.org/10.1109/TII.2019.2911064.
Hunaidi, O., W. Chu, A. Wang, and W. Guan. 2000. “Detecting leaks in plastic pipes.” J. Am. Water Works Assn. 92 (2): 82–94. https://doi.org/10.1002/j.1551-8833.2000.tb08819.x.
Hunaidi, O., and W. T. Chu. 1999. “Acoustical characteristics of leak signals in plastic water distribution pipes.” Appl. Acoust. 58 (3): 235–254. https://doi.org/10.1016/S0003-682X(99)00013-4.
Khalifa, A. E., R. Ben-Mansour, K. Youcef-Toumi, and C. Choi. 2012. “Characterization of in-pipe acoustic wave for water leak detection.” In Proc., ASME International Mechanical Engineering Congress and Exposition. New York: ASME.
Khalifa, A. E., D. M. Chatzigeorgiou, K. Youcef-Toumi, Y. A. Khulief, and R. Ben-Mansour. 2010. “Quantifying acoustic and pressure sensing for in-pipe leak detection.” In Proc., ASME Int. Mechanical Engineering Congress and Exposition, 489–495. New York: ASME.
Khulief, Y. A., and A. Khalifa. 2013. On the in-pipe measurements of acoustic signature of leaks in water pipelines. New York: ASME.
Khulief, Y. A., A. Khalifa, R. Ben Mansour, and M. A. Habib. 2012. “Acoustic detection of leaks in water pipelines using measurements inside pipe.” J. Pipeline Syst. Eng. Pract. 3 (2): 47–54. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000089.
Lai, W. W., R. K. Chang, J. F. Sham, and K. Pang. 2016. “Perturbation mapping of water leak in buried water pipes via laboratory validation experiments with high-frequency ground penetrating radar (GPR).” Tunnelling Underground Space Technol. 52 (Feb): 157–167. https://doi.org/10.1016/j.tust.2015.10.017.
Lechgar, H., A. Mallouk, M. E. I. Malaainine, T. Nahhal, and H. Rhinane. 2016. “Artificial intelligence (AI) contribution to GIS in optimal positioning of hydrophone sensors using genetic algorithm (Case study: water network, Casablanca, Morocco).” In Proc., of the Mediterranean Conf. on Information & Communication Technologies 2015, 11–19. Cham, Switzerland: Springer.
Li, R., H. Huang, K. Xin, and T. Tao. 2015. “A review of methods for burst/leakage detection and location in water distribution systems.” Water Sci. Technol. Water Supply 15 (3): 429–441. https://doi.org/10.2166/ws.2014.131.
Li, S. Z., Y. J. Song, and G. Q. Zhou. 2018. “Leak detection of water distribution pipeline subject to failure of socket joint based on acoustic emission and pattern recognition.” Measurement 115 (Feb): 39–44. https://doi.org/10.1016/j.measurement.2017.10.021.
Li, Z., L. Jing, W. Wang, P. Lee, and R. Murch. 2019. “The influence of pipeline thickness and radius on guided wave attenuation in water-filled steel pipelines: Theoretical analysis and experimental measurement.” J. Acoust. Soc. Am. 145 (1): 361–371. https://doi.org/10.1121/1.5087703.
Liemberger, R., and A. Wyatt. 2019. “Quantifying the global non-revenue water problem.” Water Supply 19 (3): 831–837. https://doi.org/10.2166/ws.2018.129.
Ma, Y., Y. Gao, X. Cui, M. J. Brennan, F. C. Almeida, and J. Yang. 2019. “Adaptive phase transform method for pipeline leakage detection.” Sensors 19 (2): 310. https://doi.org/10.3390/s19020310.
Markou, M., and S. Singh. 2003a. “Novelty detection: A review—Part 1: Statistical approaches.” Signal Process. 83 (12): 2481–2497. https://doi.org/10.1016/j.sigpro.2003.07.018.
Markou, M., and S. Singh. 2003b. “Novelty detection: A review—Part 2: Neural network based approaches.” Signal Process. 83 (12): 2499–2521. https://doi.org/10.1016/j.sigpro.2003.07.019.
Marmarokopos, K., D. Doukakis, G. Frantziskonis, and M. Avlonitis. 2018. “Leak detection in plastic water supply pipes with a high signal-to-noise ratio accelerometer.” Meas. Control 51 (1–2): 27–37. https://doi.org/10.1177/0020294018758526.
Martini, A., A. Rivola, and M. Troncossi. 2018. “Autocorrelation analysis of vibro-acoustic signals measured in a test field for water leak detection.” Appl. Sci.-Basel 8 (12): 2450. https://doi.org/10.3390/app8122450.
Martini, A., M. Troncossi, and A. Rivola. 2015. “Automatic leak detection in buried plastic pipes of water supply networks by means of vibration measurements.” Shock Vib. 2015. https://doi.org/10.1155/2015/165304.
Martini, A., M. Troncossi, and A. Rivola. 2017a. “Leak detection in water-filled small-diameter polyethylene pipes by means of acoustic emission measurements.” Appl. Sci.-Basel 7 (1): 2. https://doi.org/10.3390/app7010002.
Martini, A., M. Troncossi, and A. Rivola. 2017b. “Vibroacoustic measurements for detecting water leaks in buried small-diameter plastic pipes.” J. Pipeline Syst. Eng. Pract. 8 (4): 04017022. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000287.
Mourão, E., J. F. Pimentel, L. Murta, M. Kalinowski, E. Mendes, and C. Wohlin. 2020. “On the performance of hybrid search strategies for systematic literature reviews in software engineering.” Inf. Software Technol. 123 (Jul): 106294. https://doi.org/10.1016/j.infsof.2020.106294.
Muggleton, J., and M. Brennan. 2004. “Leak noise propagation and attenuation in submerged plastic water pipes.” J. Sound Vib. 278 (3): 527–537. https://doi.org/10.1016/j.jsv.2003.10.052.
Muggleton, J. M., M. J. Brennan, and R. J. Pinnington. 2002. “Wavenumber prediction of waves in buried pipes for water leak detection.” J. Sound Vib. 249 (5): 939–954. https://doi.org/10.1006/jsvi.2001.3881.
Muntakim, A. H., A. S. Dhar, and R. Dey. 2017. “Interpretation of acoustic field data for leak detection in ductile iron and copper water-distribution pipes.” J. Pipeline Syst. Eng. Pract. 8 (3): 05017001. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000257.
Patrício, L. D., and J. J. Ferreira. 2020. “Blockchain security research: Theorizing through bibliographic-coupling analysis.” J. Adv. Manage. Res. 18 (1): 1–35. https://doi.org/10.1108/JAMR-04-2020-0051.
Phua, W. K., S. M. Rabeek, B. Han, E. Njihof, T. T. Huang, K. T. C. Chai, J. H. H. Yeo, and S. T. Lim. 2020. “AIN-based MEMS (micro-electro-mechanical system) hydrophone sensors for IoT water leakage detection system.” Water 12 (11): 2966. https://doi.org/10.3390/w12112966.
Pinnington, R., and A. Briscoe. 1994. “Externally applied sensor for axisymmetric waves in a fluid filled pipe.” J. Sound Vib. 173 (4): 503–516. https://doi.org/10.1006/jsvi.1994.1243.
Puust, R., Z. Kapelan, D. Savic, and T. Koppel. 2010. “A review of methods for leakage management in pipe networks.” Urban Water J. 7 (1): 25–45. https://doi.org/10.1080/15730621003610878.
Sun, P., Y. Gao, B. Jin, and M. J. Brennan. 2020. “Use of PVDF wire sensors for leakage localization in a fluid-filled pipe.” Sensors 20 (3): 692. https://doi.org/10.3390/s20030692.
Xu, J. H., K. T. C. Chai, G. Q. Wu, B. B. Han, E. L. C. Wai, W. Li, J. Yeo, E. Nijhof, and Y. D. Gu. 2019. “Low-cost, tiny-sized MEMS hydrophone sensor for water pipeline leak detection.” IEEE Trans. Ind. Electron. 66 (8): 6374–6382. https://doi.org/10.1109/TIE.2018.2874583.
Zaman, D., M. K. Tiwari, A. K. Gupta, and D. Sen. 2020. “A review of leakage detection strategies for pressurised pipeline in steady-state.” Eng. Fail. Anal. 109 (Jan): 104264. https://doi.org/10.1016/j.engfailanal.2019.104264.
Zhao, M., C. Zhang, H. Liu, G. Fu, and Y. Wang. 2020. “Optimal sensor placement for pipe burst detection in water distribution systems using cost-benefit analysis.” J. Hydroinf. 22 (3): 606–618. https://doi.org/10.2166/hydro.2020.158.
Zheng, A., and A. Casari. 2018. Feature engineering for machine learning: Principles and techniques for data scientists. San Francisco: O’Reilly Media.

Information & Authors

Information

Published In

Go to Journal of Pipeline Systems Engineering and Practice
Journal of Pipeline Systems Engineering and Practice
Volume 12Issue 4November 2021

History

Published online: Jul 23, 2021
Published in print: Nov 1, 2021
Discussion open until: Dec 23, 2021

Permissions

Request permissions for this article.

Authors

Affiliations

Research Assistant, Dept. of Building and Real Estate, Hong Kong Polytechnic Univ., Hung Hom, Kowloon, Hong Kong (corresponding author). ORCID: https://orcid.org/0000-0003-4994-2253. Email: [email protected]
Tarek Zayed, F.ASCE [email protected]
Professor, Dept. of Building and Real Estate, Hong Kong Polytechnic Univ., Hung Hom, Kowloon, Hong Kong. Email: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

  • Maximum Likelihood Estimation to Localize Leaks in Water Distribution Networks, Journal of Pipeline Systems Engineering and Practice, 10.1061/JPSEA2.PSENG-1494, 14, 4, (2023).
  • Leak detection in water distribution systems by classifying vibration signals, Mechanical Systems and Signal Processing, 10.1016/j.ymssp.2022.109810, 185, (109810), (2023).
  • Evaluation of acoustic techniques for leak detection in a complex low-pressure gas pipeline network, Engineering Failure Analysis, 10.1016/j.engfailanal.2022.106897, 143, (106897), (2023).
  • Systematic and scientometric analyses of predictors for modelling water pipes deterioration, Automation in Construction, 10.1016/j.autcon.2022.104710, 149, (104710), (2023).
  • Machine learning modeling for spectral transient-based leak detection, Automation in Construction, 10.1016/j.autcon.2022.104686, 146, (104686), (2023).
  • Frequency-based leak signature investigation using acoustic sensors in urban water distribution networks, Advanced Engineering Informatics, 10.1016/j.aei.2023.101905, 55, (101905), (2023).
  • Leak Detection Methods in Water Distribution Networks: A Comparative Survey on Artificial Intelligence Applications, Journal of Pipeline Systems Engineering and Practice, 10.1061/(ASCE)PS.1949-1204.0000646, 13, 3, (2022).
  • Data-driven application of MEMS-based accelerometers for leak detection in water distribution networks, Science of The Total Environment, 10.1016/j.scitotenv.2021.151110, 809, (151110), (2022).
  • Application of fiber optics in water distribution networks for leak detection and localization: a mixed methodology-based review, H2Open Journal, 10.2166/h2oj.2021.102, 4, 1, (244-261), (2021).

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share