Generalized Acoustic Data Analysis Framework for Leakage Detection and Localization in Field Operational Water Distribution Networks
Publication: Journal of Water Resources Planning and Management
Volume 149, Issue 11
Abstract
Detecting and localizing leakages in underground water pipelines continue to be challenging in large-scale water distribution networks (WDNs) having a low density of acoustic sensors per unit pipeline. Many previous acoustic research analyses have been restricted to either (1) lab-scale WDN setups having simulated leakage conditions, or (2) field-scale WDNs with high densities of acoustic sensors. To address the current challenges, this study develops a generic and practical acoustic data analysis framework to analyze acoustics signals for leakage detection and localization in large-scale WDNs, where the proposed framework encompasses multi-stage systematic analyses, namely: (1) data quality assessment; (2) acoustic features generation and analysis; (3) leakage event detection; and (4) leakage event localization. In collaboration with Public Utility Board (PUB), Singapore’s National Water Agency, our proposed framework has been verified in large-scale WDNs in Singapore having more than 1,100 km of underground water pipelines and 82 permanently installed monitoring stations, each of which is instrumented to measure acoustics, via hydrophone equipment, pressure, and water quality parameters, and by using historical data collected between August 1, 2019 and August 31, 2020, where numerous leakage events were reported to within 1,000 m, or less, connected pipelines from independent hydrophones.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some data, models, or code generated or used during the study are proprietary or confidential in nature. All the field data including raw acoustics data files (.WAV format), detailed model configurations for the case study zones, and historical leakage records are confidential and cannot be provided without third party agreement. Parties interested in the field datasets are required to contact the corresponding author for information and authorization.
Acknowledgments
This research is supported by the Singapore National Research Foundation under its Competitive Research Programme (CRP) (Water) and administered by PUB (PUB-1804-0087), Singapore’s National Water Agency.
References
Abadi, M., P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, and M. Isard. 2016. “TensorFlow: A system for large-scale machine learning.” In Proc., 12th USENIX Symp. on Operating Systems Design and Implementation (OSDI 16), 265–283. Berkeley, CA: USENIX Association.
Ahmad, S., Z. Ahmad, C. H. Kim, and J. M. Kim. 2022. “A method for pipeline leak detection based on acoustic imaging and deep learning.” Sensors 22 (4): 1562. https://doi.org/10.3390/s22041562.
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.
Amin, M., and M. F. Ghazali. 2015. “Leakage detection in galvanized iron pipelines using ensemble empirical mode decomposition analysis.” AIP Conf. Proc. 1660 (1): 070003. https://doi.org/10.1063/1.4915721.
Chatzigeorgiou, D. M. 2010. Analysis and design of an in-pipe system for water leak detection. New York: ASME.
Chatzigeorgiou, D. M., K. Youcef-Toumi, A. E. Khalifa, and R. Ben-Mansour. 2011. “Analysis and design of an in-pipe system for water leak detection.” In Vol. 5 of Proc., ASME Design Engineering Technical Conf., 1007–1016. New York: ASME.
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., B. A. Tolson, and J. Orchard. 2020. “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.
Fares, A., I. A. Tijani, Z. Rui, and T. Zayed. 2022. “Leak detection in real water distribution networks based on acoustic emission and machine learning.” Environ. Technol. (May): 1–17. https://doi.org/10.1080/09593330.2022.2074320.
Gao, Y., F. Piltan, and J.-M. Kim. 2022. “A hybrid leak localization approach using acoustic emission for industrial pipelines.” Sensors 22 (10): 3963. https://doi.org/10.3390/s22103963.
Guo, C., K. Shi, and X. Chu. 2022. “Cross-correlation analysis of multiple fibre optic hydrophones for water pipeline leakage detection.” Int. J. Environ. Sci. Technol. 19 (1): 197–208. https://doi.org/10.1007/s13762-021-03163-y.
Guo, G., X. Yu, S. Liu, Z. Ma, Y. Wu, X. Xu, X. Wang, K. Smith, and X. Wu. 2021. “Leakage detection in water distribution systems based on time–frequency convolutional neural network.” J. Water Resour. Plann. Manage. 147 (2): 04020101. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001317.
Guo, G., X. Yu, S. Liu, X. Xu, Z. Ma, X. Wang, Y. Huang, and K. Smith. 2020. “Novel leakage detection and localization method based on line spectrum pair and cubic interpolation search.” Water Resour. Manage. 34 (12): 3895–3911. https://doi.org/10.1007/s11269-020-02651-z.
Kadri, A., A. Abu-Dayya, R. Stefanelli, and D. Trinchero. 2013. “Characterization of an acoustic wireless sensor for water leakage detection in underground pipes.” In Proc., 2013 1st Int. Conf. on Communications, Signal Processing, and Their Applications (ICCSPA), 1–5. New York: IEEE.
Kafle, M. D., S. Fong, and S. Narasimhan. 2022. “Active acoustic leak detection and localization in a plastic pipe using time delay estimation.” Appl. Acoust. 187 (Feb): 108482. https://doi.org/10.1016/j.apacoust.2021.108482.
Kafle, M. D., and S. Narasimhan. 2020. “Active acoustic leak detection in a pressurized PVC pipe.” Urban Water J. 17 (4): 315–324. https://doi.org/10.1080/1573062X.2020.1771381.
Kang, J., G. S. Member, Y. Park, J. Lee, S. Wang, and D. Eom. 2018. “Novel leakage detection by ensemble.” IEEE Trans. Ind. Electron. 65 (5): 4279–4289. https://doi.org/10.1109/TIE.2017.2764861.
Khulief, Y. A., A. Khalifa, R. B. 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.
Kothandaraman, M., Z. Law, M. A. G. Ezra, and C. H. Pua. 2020. “Adaptive independent component analysis–based cross-correlation techniques along with empirical mode decomposition for water pipeline leakage localization utilizing acousto-optic sensors.” J. Pipeline Syst. Eng. Pract. 11 (3): 04020027. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000471.
Kousiopoulos, G.-P., and S. Nikolaidis. 2022. “Acoustic method for leak size estimation in fluid-carrying pipelines.” In Proc., 2022 11th Int. Conf. on Modern Circuits and Systems Technologies (MOCAST), 1–5. New York: IEEE.
Mark, S., Z. Chi, and L. Martin. 2022. “Rate of change processing of acoustic data from a permanent monitoring system for pipe crack early identification: A case study.” J. Water Resour. Plann. Manage. 148 (2): 05021031. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001517.
Mark, S., J. Gong, C. Zhang, A. Marchi, L. Dix, and M. F. Lambert. 2020. “Leak-before-break main failure prevention for water distribution pipes using acoustic smart water technologies: Case study in Adelaide.” J. Water Resour. Plann. Manage. 146 (10): 05020020. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001266.
Mounce, R., B. Boxall, and J. Machell. 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. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000030.
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.
Ning, F., Z. Cheng, D. Meng, and J. Wei. 2021. “A framework combining acoustic features extraction method and random forest algorithm for gas pipeline leak detection and classification.” Appl. Acoust. 182 (Nov): 108255. https://doi.org/10.1016/j.apacoust.2021.108255.
Pedregosa, F., et al. 2011. “Scikit-learn: Machine learning in Python.” J. Mach. Learn. Res. 12 (Nov): 2825–2830.
Ting, L. L., J. Y. Tey, A. C. Tan, Y. J. King, and A. R. Faidz. 2019. “Improvement of acoustic water leak detection based on dual tree complex wavelet transform-correlation method.” IOP Conf. Ser.: Earth Environ. Sci. 268 (1): 012025. https://doi.org/10.1088/1755-1315/268/1/012025.
Wols, B. A., K. Van Daal, and P. Van Thienen. 2014. “Effects of climate change on drinking water distribution network integrity: Predicting pipe failure resulting from differential soil settlement.” Procedia Eng. 70 (Jan): 1726–1734. https://doi.org/10.1016/j.proeng.2014.02.190.
Wols, B. A., and P. Van Thienen. 2014. “Modelling the effect of climate change induced soil settling on drinking water distribution pipes.” Comput. Geotech. 55 (Jan): 240–247. https://doi.org/10.1016/j.compgeo.2013.09.003.
Wu, Z. Y., A. Chew, X. Meng, J. Cai, J. Pok, R. Kalfarisi, K. C. Lai, S. F. Hew, and J. J. Wong. 2022. “Data-driven and model-based framework for smart water grid anomaly detection and localization.” AQUA Water Infrastruct. Ecosyst. Soc. 71 (1): 31–41. https://doi.org/10.2166/aqua.2021.091.
Wu, Z. Y., M. Farley, D. Turtle, Z. Kapelan, J. Boxall, S. Mounce, S. Dahasahasra, M. Mulay, and Y. Kleiner. 2011. Water loss reduction. Drive Exton, PA: Bentley Institute Press.
Wu, Z. Y., and Y. He. 2021. “Time series data decomposition-based anomaly detection and evaluation framework for operational management of smart water grid.” J. Water Resour. Plann. Manage. 147 (9): 04021059. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001433.
Xue, Z., L. Tao, J. Fuchun, E. Riehle, H. Xiang, N. Bowen, and R. P. Singh. 2020. “Application of acoustic intelligent leak detection in an urban water supply pipe network.” J. Water Supply Res. Technol. AQUA 69 (5): 512–520. https://doi.org/10.2166/aqua.2020.022.
Yang, J., Y. Wen, and P. Li. 2008. “Leak location using blind system identification in water distribution pipelines.” J. Sound Vib. 310 (1–2): 134–148. https://doi.org/10.1016/j.jsv.2007.07.067.
Zhang, C., M. F. Lambert, M. L. Stephens, J. Gong, and B. S. Cazzolato. 2020. “Pipe crack early warning for burst prevention by permanent acoustic noise level monitoring in smart water networks.” Urban Water J. 17 (9): 827–837. https://doi.org/10.1080/1573062X.2020.1828501.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 149 • Issue 11 • November 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jan 19, 2023
Accepted: May 5, 2023
Published online: Aug 23, 2023
Published in print: Nov 1, 2023
Discussion open until: Jan 23, 2024
ASCE Technical Topics:
- Acoustics
- Continuum mechanics
- Data analysis
- Detection methods
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Environmental engineering
- Infrastructure
- Methodology (by type)
- Pipe leakage
- Pipeline management
- Pipeline systems
- Pipelines
- Pressure (type)
- Research methods (by type)
- Solid mechanics
- Water and water resources
- Water leakage and water loss
- Water management
- Water pipelines
- Water pressure
- Water quality
- Water supply
- Water supply systems
- Water treatment
Authors
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.