A field implementation of linear prediction for leak-monitoring in water distribution networks

Abstract Water distribution networks (WDNs) are complex systems that are subjected to stresses due to a number of hydraulic and environmental loads. As a result, system leaks remain an unavoidable reality. Leaks which are not large enough to become visible at the street level can often go undetected for prolonged periods of time; the presence of smaller leaks can be concealed in system variability. The current paper addresses the problem of leak-detection and localization in WDNs, using a data-driven methodology which utilizes linear prediction (LP) theory. LP has a relatively simple mathematical formulation and has been shown in laboratory studies to effectively capture leak-induced signatures in fluid-filled pipes. In this paper, the performance of LP for leak-detection is verified, using field data in an operational WDN. In addition, a two-part localization approach is proposed which utilizes LP pre-processed data, in tandem with the traditional cross-correlation approach. Results of the field study show that the proposed method is able to perform both leak-detection and localization in full-scale systems using relatively short time signal lengths. This is advantageous in continuous monitoring situations as this minimizes data transmission requirements, which are one of the main impediments to full-scale implementation and deployment of leak-detection technology. In addition to the analysis results, a novel hydrant-mounted data-acquisition system is proposed, along with its unique hardware and software capabilities.

[1]  Andrew J. Whittle,et al.  WaterWiSe@SG : a testbed for continuous monitoring of the water distribution system in Singapore , 2011 .

[2]  A. Lambert,et al.  Accounting for losses: the bursts and background concept , 1994 .

[3]  M. Iqbal,et al.  Sensor Networks for Monitoring and Control of Water Distribution Systems , 2013 .

[4]  R. J. Pinnington,et al.  Externally Applied Sensor for Axisymmetric Waves in a Fluid Filled Pipe , 1994 .

[5]  Osama Moselhi,et al.  Automated Detection and Location of Leaks in Water Mains Using Infrared Photography , 2010 .

[6]  Mehrdad Sharif Bakhtiar,et al.  LEAK DETECTION IN WATER-FILLED PLASTIC PIPES THROUGH THE APPLICATION OF TUNED WAVELET TRANSFORMS TO ACOUSTIC EMISSION SIGNALS , 2010 .

[7]  Ian F. C. Smith,et al.  Performance comparison of reduced models for leak detection in water distribution networks , 2015, Adv. Eng. Informatics.

[8]  Nicole Metje,et al.  SmartPipes: Smart Wireless Sensor Networks for Leak Detection in Water Pipelines , 2014, J. Sens. Actuator Networks.

[9]  Francesco Piazza,et al.  A Statistical Framework for Automatic Leakage Detection in Smart Water and Gas Grids , 2016 .

[10]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[11]  Joby Boxall,et al.  Development and Verification of an Online Artificial Intelligence System for Detection of Bursts and Other Abnormal Flows , 2010 .

[12]  Roya A. Cody,et al.  Leak detection in water distribution pipes using singular spectrum analysis , 2018, Urban Water Journal.

[13]  Zhihua Zhang,et al.  Learning a multivariate Gaussian mixture model with the reversible jump MCMC algorithm , 2004, Stat. Comput..

[14]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[15]  L. Mays.  Ancient water technologies , 2010 .

[16]  Michael J. Brennan,et al.  A model of the correlation function of leak noise in buried plastic pipes , 2004 .

[17]  Ian F. C. Smith,et al.  Model falsification diagnosis and sensor placement for leak detection in pressurized pipe networks , 2013, Adv. Eng. Informatics.

[18]  Francisco Herrera,et al.  A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability , 2009, Soft Comput..

[19]  James Durbin,et al.  The fitting of time series models , 1960 .

[20]  J. Makhoul,et al.  Linear prediction: A tutorial review , 1975, Proceedings of the IEEE.

[21]  Suzhen Li,et al.  Leak detection of water distribution pipeline subject to failure of socket joint based on acoustic emission and pattern recognition , 2018 .

[22]  Michael J. Brennan,et al.  Leak noise propagation and attenuation in submerged plastic water pipes , 2004 .