Leakage Detection in Water Distribution Systems Based on Time–Frequency Convolutional Neural Network
Publication: Journal of Water Resources Planning and Management
Volume 147, Issue 2
Abstract
Effectively detecting leaks is critical to improving leakage control management. Acoustic detection is one of the main leakage detection methods, and has been widely used in water utilities. Nevertheless, the effectiveness of this method is unsatisfactory in cases with various types of noise. To tackle this problem, this work proposes a leakage spectrogram to represent the features of leakage signals, and developed a time–frequency convolutional neural network (TFCNN) model to identify leakage signals. The performance of the TFCNN model was compared with other classification models (i.e., decision tree, support vector machine, multilayer perceptron, random forest, and extreme gradient boosting) under different signal-to-noise ratio (SNR) conditions. The results showed that the proposed method improves the accuracy and stability of leakage detection. Compared with other classification models, the TFCNN model had the best performance, and its mean accuracy reached 98% under different SNR conditions. Even in the case of SNR, the mean detection accuracy reached 90%. In practice, the mean detection accuracy reached 99% for different time–frequency resolutions. The transfer learning–based TFCNN model is a promising method for leakage detection in cases in which there are insufficient data sets.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request: data of leakage and interference under different SNR conditions. In addition, the models and codes are available at https://github.com/ggc19/Paper-codes.
Acknowledgments
This work was jointly supported by the National Natural Science Foundation of China (51879139) and Tsinghua University School of Environment (SOE)—Xingrong Group Joint Research Center for Advanced Water Technology.
References
Adedeji, K. B., Y. Hamam, B. T. Abe, and A. M. Abu-Mahfouz. 2017. “Towards achieving a reliable leakage detection and localization algorithm for application in water piping networks: An overview.” IEEE Access 5 (10): 20272–20285. https://doi.org/10.1109/ACCESS.2017.2752802.
Ahadi, M., and M. S. Bakhtiar. 2010. “Leak detection in water-filled plastic pipes through the application of tuned wavelet transforms to Acoustic Emission signals.” Appl. Acoust. 71 (7): 634–639. https://doi.org/10.1016/j.apacoust.2010.02.006.
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.
Amran, T. S. T., M. P. Ismail, M. R. Ahmad, M. S. M. Amin, S. Sani, N. A. Masenwat, M. A. Ismail, and S. H. A. Hamid. 2017. “Detection of underground water distribution piping system and leakages using ground penetrating radar (GPR).” In Vol. 1799 of Proc., AIP Conf., 030004. Melville, NY: American Institute of Physics. https://doi.org/10.1063/1.4972914.
Boaz, L., S. Kaijage, and R. Sinde. 2014. “An overview of pipeline leak detection and location systems.” In Proc., 2014 Pan African Conf. on Science, Computing and Telecommunications, 133–137. New York: IEEE. https://doi.org/10.1109/SCAT.2014.7055147.
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., G. Meyers, V. Meruane, R. P. Collins, and S. B. M. Beck. 2018. “Experimental investigation into techniques to predict leak shapes in water distribution systems using vibration measurements.” J. Hydroinf. 20 (4): 815–828. https://doi.org/10.2166/hydro.2018.117.
Cody, R. A., P. Dey, and S. Narasimhan. 2020. “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.
Colombo, A. F., and B. W. Karney. 2002. “Energy and costs of leaky pipes: Toward comprehensive picture.” J. Water Resour. Plann. Manage. 128 (6): 441–450. https://doi.org/10.1061/(ASCE)0733-9496(2002)128:6(441).
Feng, T., and S. Yang. 2018. “Speech emotion recognition based on LSTM and Mel scale wavelet packet decomposition.” In Proc., 2018 Int. Conf. on Algorithms, Computing and Artificial Intelligence, 1–7. New York: Association for Computing Machinery. https://doi.org/10.1145/3302425.3302444.
Ferrante, M., B. Brunone, and S. Meniconi. 2007. “Wavelets for the analysis of transient pressure signals for leak detection.” J. Hydraul. Eng. 133 (11): 1274–1282. https://doi.org/10.1061/(ASCE)0733-9429(2007)133:11(1274).
Gao, Y., M. J. Brennan, P. Joseph, J. Muggleton, and O. Hunaidi. 2004. “A model of the correlation function of leak noise in buried plastic pipes.” J. Sound Vib. 277 (1–2): 133–148. https://doi.org/10.1016/j.jsv.2003.08.045.
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., 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.
Gong, W., M. A. Suresh, L. Smith, A. Ostfeld, R. Stoleru, A. Rasekh, and M. K. Banks. 2016. “Mobile sensor networks for optimal leak and backflow detection and localization in municipal water networks.” Environ. Modell. Software 80 (Jun): 306–321. https://doi.org/10.1016/j.envsoft.2016.02.001.
Guo, C., Y. Wen, P. Li, and J. Wen. 2016. “Adaptive noise cancellation based on EMD in water supply pipeline leak detection.” Measurement 79 (Feb): 188–197. https://doi.org/10.1016/j.measurement.2015.09.048.
Guo, G., S. Liu, Y. Wu, J. Li, R. Zhou, and X. Zhu. 2018. “Short-term water demand forecast based on deep learning method.” J. Water Resour. Plann. Manage. 144 (12): 04018076. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000992.
Jalil, M., F. A. Butt, and A. Malik. 2013. “Short-time energy, magnitude, zero crossing rate and autocorrelation measurement for discriminating voiced and unvoiced segments of speech signals.” In Proc., 2013 Int. Conf. on Technological Advances in Electrical, Electronics and Computer Engineering, 208–212. New York: IEEE. https://doi.org/10.1109/TAEECE.2013.6557272.
Jin, L., and J. Cheng. 2010. “An improved speech endpoint detection based on spectral subtraction and adaptive sub-band spectral entropy.” In Proc., 2010 Int. Conf. on Intelligent Computation Technology and Automation, 591–594. Los Alamitos, CA: IEEE Computer Society. https://doi.org/10.1109/ICICTA.2010.309.
Kaiser, J. F. 1990. “On a simple algorithm to calculate the ‘energy’ of a signal.” In Proc., Int. Conf. on Acoustics, Speech, and Signal Processing. 381–384. New York: IEEE. https://doi.org/10.1109/ICASSP.1990.115702.
Kang, J., Y.-J. Park, J. Lee, S.-H. Wang, and D.-S. Eom. 2018. “Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems.” IEEE Trans. Ind. Electron. 65 (5): 4279–4289. https://doi.org/10.1109/TIE.2017.2764861.
Kim, M.-S., and S.-K. Lee. 2009. “Detection of leak acoustic signal in buried gas pipe based on the time–frequency analysis.” J. Loss Prev. Process Ind. 22 (6): 990–994. https://doi.org/10.1016/j.jlp.2008.08.009.
Lay-Ekuakille, A., G. Vendramin, and A. Trotta. 2009. “Spectral analysis of leak detection in a zigzag pipeline: A filter diagonalization method-based algorithm application.” Measurement 42 (3): 358–367. https://doi.org/10.1016/j.measurement.2008.07.007.
LeCun, Y., and Y. Bengio. 1998. “Convolutional networks for images, speech, and time series.” In Vol. 3361 of The handbook of brain theory and neural networks, 1995. Cambridge, MA: MIT Press.
Li, S., Y. Song, and G. 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.
Meseguer, J., J. M. Mirats-Tur, G. Cembrano, V. Puig, J. Quevedo, R. Pérez, G. Sanz, and D. Ibarra. 2014. “A decision support system for on-line leakage localization.” Environ. Modell. Software 60 (Oct): 331–345. https://doi.org/10.1016/j.envsoft.2014.06.025.
Mounce, S. R., J. 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.
Ni, L., J. Jiang, Y. Pan, and Z. Wang. 2014. “Leak location of pipelines based on characteristic entropy.” J. Loss Prev. Process Ind. 30 (Jul): 24–36. https://doi.org/10.1016/j.jlp.2014.04.004.
Pan, S. J., and Q. Yang. 2010. “A survey on transfer learning.” IEEE Trans. Knowl. Data Eng. 22 (10): 1345–1359. https://doi.org/10.1109/TKDE.2009.191.
Pedregosa, F., et al. 2011. “Scikit-learn: Machine learning in Python.” J. Mach. Learn. Res. 12 (85): 2825–2830.
Puust, R., Z. Kapelan, D. A. 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.
Qi, Z., F. Zheng, D. Guo, H. R. Maier, T. Zhang, T. Yu, and Y. Shao. 2018a. “Better understanding of the capacity of pressure sensor systems to detect pipe burst within water distribution networks.” J. Water Resour. Plann. Manage. 144 (7): 04018035. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000957.
Qi, Z., F. Zheng, D. Guo, T. Zhang, Y. Shao, T. Yu, K. Zhang, and H. R. Maier. 2018b. “A comprehensive framework to evaluate hydraulic and water quality impacts of pipe breaks on water distribution systems.” Water Resour. Res. 54 (10): 8174–8195. https://doi.org/10.1029/2018WR022736.
Qian, S., and D. Chen. 1999. “Joint time-frequency analysis.” IEEE Signal Process Mag. 16 (2): 52–67. https://doi.org/10.1109/79.752051.
Sarkamaryan, S., A. Haghighi, and A. Adib. 2018. “Leakage detection and calibration of pipe networks by the inverse transient analysis modified by Gaussian functions for leakage simulation.” J. Water Supply Res. Technol. AQUA 67 (4): 404–413. https://doi.org/10.2166/aqua.2018.176.
Schmidhuber, J. 2015. “Deep learning in neural networks: An overview.” Neural Network 61 (Jan): 85–117. https://doi.org/10.1016/j.neunet.2014.09.003.
Sergey, I., and C. Szegedy. 2015. “Batch normalization: Accelerating deep network training by reducing internal covariate shift.” Preprint, submitted February 11, 2015. http://arxiv.org/abs/1502.03167.
Shao, Y., X. Li, T. Zhang, S. Chu, and X. Liu. 2019. “Time-series-based leakage detection using multiple pressure sensors in water distribution systems.” Sensors 19 (14): 3070. https://doi.org/10.3390/s19143070.
Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. 2014. “Dropout: A simple way to prevent neural networks from overfitting.” J. Mach. Learn. Res. 15 (1): 1929–1958. https://dl.acm.org/doi/10.5555/2627435.2670313.
Sun, J., Q. Xiao, J. Wen, and Y. Zhang. 2016. “Natural gas pipeline leak aperture identification and location based on local mean decomposition analysis.” Measurement 79 (Feb): 147–157. https://doi.org/10.1016/j.measurement.2015.10.015.
Wen, Y., P. Li, J. Yang, and Z. Zhou. 2004. “Information processing in buried pipeline leak detection system.” In Proc., Int. Conf. on Information Acquisition, 489–493. New York: IEEE. https://doi.org/10.1109/ICIA.2004.1373418.
Xu, J., K. T.-C. Chai, G. Wu, B. Han, E. L.-C. Wai, W. Li, J. Yeo, E. Nijhof, and Y. 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.
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, P. Li, and X. Wang. 2013. “Study on an improved acoustic leak detection method for water distribution systems.” Urban Water J. 10 (2): 71–84. https://doi.org/10.1080/1573062X.2012.699071.
Yosinski, J., J. Clune, Y. Bengio, and H. Lipson. 2014. “How transferable are features in deep neural networks?” In Vol. 27 of Advances in neural information processing systems, 3320–3328. La Jolla, CA: Neural Information Processing Systems.
Zhou, B., V. Lau, and X. Wang. 2019a. “Machine-learning-based leakage-event identification for smart water supply systems.” IEEE Internet Things J. 7 (3): 2277–2292. https://doi.org/10.1109/JIOT.2019.2958920.
Zhou, X., Z. Tang, W. Xu, F. Meng, X. Chu, K. Xin, and G. Fu. 2019b. “Deep learning identifies accurate burst locations in water distribution networks.” Water Res. 166 (Dec): 115058. https://doi.org/10.1016/j.watres.2019.115058.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 147 • Issue 2 • February 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Dec 7, 2019
Accepted: Aug 28, 2020
Published online: Nov 20, 2020
Published in print: Feb 1, 2021
Discussion open until: Apr 20, 2021
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.