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

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.Singapore-MIT Alliance for Research and Technolog

Water quality event detection and customer complaint clustering analysis in distribution systems

Safe, clean drinking water is a foundation of society and water quality monitoring can contribute to ensuring this. A case study application of the CANARY software to historic data from a UK drinking water distribution system is described. Sensitivity studies explored appropriate choice of algorithmic parameter settings for a baseline site, performance was evaluated with artificial events and the system then transferred to all sites. Results are presented for analysis of nine water quality sensors measuring six parameters and deployed in three connected district meter areas (DMAs), fed from a single water source (service reservoir), for a 1 year period and evaluated using comprehensive water utility records with 86% of event clusters successfully correlated to causes (spatially limited to DMA level). False negatives, defined by temporal clusters of water quality complaints in the pilot area not corresponding to detections, were only approximately 25%. It was demonstrated that the software could be configured and applied retrospectively (with potential for future near real time application) to detect various water quality event types (with a wider remit than contamination alone) for further interpretation

Alternative Strategies For Optimal Water Quality Sensor Placement In Drinking Water Distribution Networks

The most commonly applied strategies for optimal water quality sensor placement in drinking water distribution systems are aimed at contamination early warning systems. These strategies aim to minimize the number of people affected in case of a deliberate contamination of drinking water in the distribution system, and provide a valuable tool. A number of factors which are usually not taken into account, including the response strategy to the identification of a contamination event, the fallibility of sensors and changes in network configuration (valve manipulation) and operation, may affect the results of these strategies. Since the quickness and effectiveness of a response is generally also a function of the location of the contamination event (both source and first detection), knowledge on the response strategy should also be part of the sensor placement optimization methodology. Hydraulic models generally play a central role in the optimization of sensor placement. The validity of their computations strongly depends upon accurate and up to date information on the network, which is often not fully available (e.g. unregistered valve status changes). Therefore, a sensor network configuration which is somewhat robust to these issues is desirable. Besides contamination early warning systems, there are several other reasons for placing water quality sensors in distribution network, including process control and monitoring, regulatory monitoring, etc. These require a different approach to optimization of the sensor network in terms of sensor locations. In this paper, we demonstrate the application of different sensor location optimization strategies in drinking water distribution networks, with aims such as minimization of the number of people affected, maximization of distribution network coverage, optimization of sensor network robustness and optimization of contamination source identification. We present and compare results of these different approaches applied to hydraulic models of a real drinking water distribution network in the Netherlands

Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location

[EN] A large volume of the water produced for public supply is lost in the systems between sources and consumers. An important-in many cases the greatest-fraction of these losses are physical losses, mainly related to leaks and bursts in pipes and in consumer connections. Fast detection and location of bursts plays an important role in the design of operation strategies for water loss control, since this helps reduce the volume lost from the instant the event occurs until its effective repair (run time). The transient pressure signals caused by bursts contain important information about their location and magnitude, and stamp on any of these events a specific "hydraulic signature". The present work proposes and evaluates three methods to disaggregate transient signals, which are used afterwards to train artificial neural networks (ANNs) to identify burst locations and calculate the leaked flow. In addition, a clustering process is also used to group similar signals, and then train specific ANNs for each group, thus improving both the computational efficiency and the location accuracy. The proposed methods are applied to two real distribution networks, and the results show good accuracy in burst location and characterization.Manzi, D.; Brentan, BM.; Meirelles, G.; Izquierdo Sebastián, J.; Luvizotto Jr., E. (2019). Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location. Water. 11(11):1-13. https://doi.org/10.3390/w11112279S1131111Creaco, E., & Walski, T. (2017). Economic Analysis of Pressure Control for Leakage and Pipe Burst Reduction. Journal of Water Resources Planning and Management, 143(12), 04017074. doi:10.1061/(asce)wr.1943-5452.0000846Campisano, A., Creaco, E., & Modica, C. (2010). RTC of Valves for Leakage Reduction in Water Supply Networks. Journal of Water Resources Planning and Management, 136(1), 138-141. doi:10.1061/(asce)0733-9496(2010)136:1(138)Campisano, A., Modica, C., Reitano, S., Ugarelli, R., & Bagherian, S. (2016). Field-Oriented Methodology for Real-Time Pressure Control to Reduce Leakage in Water Distribution Networks. Journal of Water Resources Planning and Management, 142(12), 04016057. doi:10.1061/(asce)wr.1943-5452.0000697Vítkovský, J. P., Simpson, A. R., & Lambert, M. F. (2000). Leak Detection and Calibration Using Transients and Genetic Algorithms. Journal of Water Resources Planning and Management, 126(4), 262-265. doi:10.1061/(asce)0733-9496(2000)126:4(262)Pérez, R., Puig, V., Pascual, J., Quevedo, J., Landeros, E., & Peralta, A. (2011). Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks. Control Engineering Practice, 19(10), 1157-1167. doi:10.1016/j.conengprac.2011.06.004Jung, D., & Kim, J. (2017). Robust Meter Network for Water Distribution Pipe Burst Detection. Water, 9(11), 820. doi:10.3390/w9110820Colombo, A. F., Lee, P., & Karney, B. W. (2009). A selective literature review of transient-based leak detection methods. Journal of Hydro-environment Research, 2(4), 212-227. doi:10.1016/j.jher.2009.02.003Choi, D., Kim, S.-W., Choi, M.-A., & Geem, Z. (2016). Adaptive Kalman Filter Based on Adjustable Sampling Interval in Burst Detection for Water Distribution System. Water, 8(4), 142. doi:10.3390/w8040142Christodoulou, S. E., Kourti, E., & Agathokleous, A. (2016). Waterloss Detection in Water Distribution Networks using Wavelet Change-Point Detection. Water Resources Management, 31(3), 979-994. doi:10.1007/s11269-016-1558-5Guo, X., Yang, K., & Guo, Y. (2012). Leak detection in pipelines by exclusively frequency domain method. Science China Technological Sciences, 55(3), 743-752. doi:10.1007/s11431-011-4707-3Holloway, M. B., & Hanif Chaudhry, M. (1985). Stability and accuracy of waterhammer analysis. Advances in Water Resources, 8(3), 121-128. doi:10.1016/0309-1708(85)90052-1Sanz, G., Pérez, R., Kapelan, Z., & Savic, D. (2016). Leak Detection and Localization through Demand Components Calibration. Journal of Water Resources Planning and Management, 142(2), 04015057. doi:10.1061/(asce)wr.1943-5452.0000592Zhang, Q., Wu, Z. Y., Zhao, M., Qi, J., Huang, Y., & Zhao, H. (2016). Leakage Zone Identification in Large-Scale Water Distribution Systems Using Multiclass Support Vector Machines. Journal of Water Resources Planning and Management, 142(11), 04016042. doi:10.1061/(asce)wr.1943-5452.0000661Mounce, S. R., & Machell, J. (2006). Burst detection using hydraulic data from water distribution systems with artificial neural networks. Urban Water Journal, 3(1), 21-31. doi:10.1080/15730620600578538Covas, D., Ramos, H., & de Almeida, A. B. (2005). Standing Wave Difference Method for Leak Detection in Pipeline Systems. Journal of Hydraulic Engineering, 131(12), 1106-1116. doi:10.1061/(asce)0733-9429(2005)131:12(1106)Liggett, J. A., & Chen, L. (1994). Inverse Transient Analysis in Pipe Networks. Journal of Hydraulic Engineering, 120(8), 934-955. doi:10.1061/(asce)0733-9429(1994)120:8(934)Caputo, A. C., & Pelagagge, P. M. (2002). An inverse approach for piping networks monitoring. Journal of Loss Prevention in the Process Industries, 15(6), 497-505. doi:10.1016/s0950-4230(02)00036-0Van Zyl, J. E. (2014). Theoretical Modeling of Pressure and Leakage in Water Distribution Systems. Procedia Engineering, 89, 273-277. doi:10.1016/j.proeng.2014.11.187Izquierdo, J., & Iglesias, P. . (2004). Mathematical modelling of hydraulic transients in complex systems. Mathematical and Computer Modelling, 39(4-5), 529-540. doi:10.1016/s0895-7177(04)90524-9Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007). Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2), 107-144. doi:10.1007/s10618-007-0064-zNavarrete-López, C., Herrera, M., Brentan, B., Luvizotto, E., & Izquierdo, J. (2019). Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework. Water, 11(2), 246. doi:10.3390/w11020246Meirelles, G., Manzi, D., Brentan, B., Goulart, T., & Luvizotto, E. (2017). Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks. Water Resources Management, 31(13), 4339-4351. doi:10.1007/s11269-017-1750-2Adamowski, J., & Chan, H. F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1-4), 28-40. doi:10.1016/j.jhydrol.2011.06.013Brentan, B., Meirelles, G., Luvizotto, E., & Izquierdo, J. (2018). Hybrid SOM+ k -Means clustering to improve planning, operation and management in water distribution systems. 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Identifying Sampling Interval for Event Detection in Water Distribution Networks

It is a generally adopted policy, albeit unofficially, to sample flow and pressure data at a 15-min interval for water distribution system hydraulic measurements. Further, for flow, this is usually averaged, whereas pressure is instantaneous. This paper sets out the findings of studies into the potential benefits of a higher sampling rate and averaging for flow and pressure measurements in water distribution systems. A data set comprising sampling at 5 s (in the case of pressure), 1 min, 5 min, and 15 min, both instantaneous and averaged, for a set of flow and pressure sensors deployed within two DMAs has been used. Engineered events conducted by opening fire hydrants/wash outs were used to form a controlled baseline detection comparison with known event start times. A data analysis system using support vector regression (SVR) was used to analyze the flow and pressure time series data from the deployed sensors and hence, detect these abnormal events. Results are analyzed over different sensors and events. The overall trend in the results is that a faster sampling rate leads to earlier event detection. However, it is concluded that a sampling interval of 1 or 5 min does not significantly improve detection to the point at which it is worth the added increase in power, communications, and data management requirements with current technologies. It was discovered that averaging pressure data can result in more rapid detection when compared with using the same instantaneous sampling rate. Averaging of pressure data is also likely to provide better regulatory compliance and provide improved data for EPS hydraulic modelling. This improvement can be achieved without any additional overheads on communications by a simple firmware alteration and hence, is potentially a very low cost upgrade with significant gains

Systems and processes for early detection of biological ammonia oxidation in water using fluorometry

This invention relates generally to a system and process for early detection of biological ammonia oxidation in water utilizing a fluorescence-based sensor and process. Various embodiments are configured to read increases in a fluorescence excitation-emission wavelength pair that is responsive to a period of time (days to weeks or even longer) prior to the onset of biological ammonia oxidation, which is considered to be a nitrification event. Fluorescence excitation/emission pairs that have proven to be reliable include a fluorescence excitation wavelength of about 230 nm and an emission wavelength of about 345 nm and an excitation wavelength of 325 and an emission wavelength of 470. The system and process enable drinking water utilities to improve management of its distribution systems and facilitate earlier corrective actions, resulting is less loss of treated water through flushing and other tangible benefits

Algorithms to mimic human interpretation of turbidity events from drinking water distribution systems

Deriving insight from the increasing volume of water quality time series data from drinking water distribution systems is complex and is usually situation- and individual-specific. This research used crowd-sourcing exercises involving groups of domain experts to identify features of interest within turbidity time series data from operational systems. The resulting labels provide insight and a novel benchmark against which algorithmic approaches to mimic the human interpretation could be evaluated. Reflection of the results of the labelling exercises resulted in the proposal of a turbidity event scale consisting of advisory 4 NTU levels to inform utility response. Automation, for scale up, was designed to enable event detection within these categories, with the <2NTU category being the most challenging. A time-based averaging approach, based on data at the same time of day, was found to be most effective for identifying these advisory events. The automation of event detection and categorisation presented here provides the opportunity to gain actionable insight to safeguard drinking water quality from ageing infrastructure

Burst Detection and Localization using Discrete Wavelet Transform and Cross-Correlation

[ES] La ruptura súbita en los sistemas de distribución de agua provoca gran pérdida de este recurso natural, interrumpe el abastecimiento, daña las calles y edificaciones y aumenta la transmisión de enfermedades infecciosas. En este artículo se propone un nuevo algoritmo que permite la detección y localización automática de rupturas súbitas en los sistemas de distribución de agua. En cuanto a la detección, la novedad consiste en usar el criterio de correlación wavelet para computar la decisión estadística y compararla con un umbral de detección. La novedad en la localización consiste en usar el operador estadístico correlación cruzada. El algoritmo se implementó en Octave y fue validado con 32 señales adquiridas en el laboratorio en una tubería de acero de 26.7 m de longitud. En 16 señales se provocó ruptura súbita las cuales fueron detectadas bajo una probabilidad de falsos positivos de 2 %. No se presentaron falsos positivos en las 16 señales donde solamente estaba la presencia de ruido.[EN] Burst in water distribution systems causes great loss of this natural resource, interrupts the water supply, damages the streets, builds and increases the transmission of infectious diseases. In this paper we propose a new algorithm that allows the detection and automatic localization of burst in water distribution systems. As for detection, the novelty is to use the wavelet correlation criterion to compute the statistical decision and compare it with a detection threshold. The novelty in the localization is to use the statistical operator cross-correlation. The algorithm was implemented in Octave and was validated with 32 signals acquired in the laboratory in a 26.7 m long steel pipe. In 16 signals burst were triggered which were detected under a false positive probability of 2 %. No false positives were present on the 16 signals where only noise was present.Trutié-Carrero, E.; Valdés-Santiago, D.; León-Mecías, Á.; Ramírez-Beltrán, J. (2018). Detección y Localización de Ruptura Súbita mediante Transformada Wavelet Discreta y Correlación Cruzada. Revista Iberoamericana de Automática e Informática industrial. 15(2):211-216. https://doi.org/10.4995/riai.2017.8738OJS211216152Cedeño, A., Trujillo, R., 2013. Estudio comparativo de técnicas de reducción de ruido en se-ales industriales mediante transformada wavelet discreta y selección adaptativa del umbral. Revista Iberoamericana de Automática e Informática Industrial RIAI 10, 143-148. https://doi.org/10.1016/j.riai.2013.03.003Donoho, D. L., Johnstone, J. M., 1994. Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 425-455. https://doi.org/10.1093/biomet/81.3.425Eaton, J. W., Bateman, D., Hauberg, S., Wehbring, R., 2014. GNU Octave version 3.8.1 manual: a high-level interactive language for numerical computations. CreateSpace Independent Publishing Platform.Ebacher, G., Besner, M.-C., Prévost, M., Allard, D., 2010. Negative pressure events in water distribution systems: Public health risk assessment based on transient analysis outputs. In: Water Distribution Systems Analysis 2010. pp. 471-483.Grinstead, C. M., Snell, J. L., 1997. Introduction to Probability. American Mathematical Society.Luo, Jun; Liu, G. H. Z., 2016. Damage detection for shear structures based on wavelet spectral transmissibility matrices under nonstationary stochastic excitation. Structural Control and Health Monitoring. https://doi.org/10.1002/stc.1862Mallat, S., 1999. A Wavelet Tour of Signal Processing. Academic Press. Martini, A., Troncossi, M., Rivola, A., 2013. Vibration monitoring as a tool for leak detection in water distribution networks. In: International Conference Surveillance 7.Meniconi, S., Brunone, B., Ferrante, M., Capponi, C., Pedroni, M., Zaghini, M., Leoni, F., 2014. Transmission Main Survey by Transient Tests: The Case of Villanova Plan in Mantova (I). Procedia Engineering 89. https://doi.org/10.1016/j.proeng.2014.11.454Mounce, Stephen R.; Mounce, R. B. B. J. B., 03 2012. Identifying sampling interval for event detection in water distribution networks. Journal of Water Resources Planning and Management 138. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000170Oppenheim, A. V., Schafer, R. W., 2010. Discrete-Time Signal Processing. Prentice Hall.Proakis, J. G., Manolakis, D. G., 2006. Digital signal processing: principles, algorithms, and applications. Prentice-Hall.Rathnayaka, S., Shannon, B., Rajeev, P., Kodikara, J., 2016. Monitoring of pressure transients in water supply networks. Water Resources Management 30 (2), 471-485. https://doi.org/10.1007/s11269-015-1172-ySrirangarajan, S., Allen, M., Preis, A., 2013. Wavelet-based burst event detection and localization in water distribution systems. Journal of Signal Processing Systems 72, 1-16. https://doi.org/10.1007/s11265-012-0690-6Srirangarajan, S., Iqbal, M., Lim, H. B., Allen, M., Preis, A., Whittle, A. J., 2011. Water main burst event detection and localization. In: Water Distribution Systems Analysis 2010. Tucson, Arizona, United States. https://doi.org/10.1061/41203(425)119Ye, G., Fenner, R. A., 2011. Kalman filtering of hydraulic measurements for burst detection in water distribution systems. Journal of Pipeline Systems Engineering and Practice 2, 14-22. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000070Ye, G., Fenner, R. A., 2014. Study of burst alarming and data sampling frequency in water distribution networks. 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Online Monitoring Framework for Pressure Transient Detection in Water Distribution Networks

Access to potable drinking water is a necessity and basic human right. Most North Americans obtain treated water through water distribution networks, an essential part of municipal infrastructure that is subject to decay and degradation. Amongst the factors influencing pipe failure are events that trigger abrupt pressure changes, or transients, which can cause pipe breakages in the short term, and general fatigue in the long term. The ability to quantify these transients as they occur is important for effective asset management, and for preventing and mitigating the occurrence of failure. Current practices take a largely reactive approach to event detection, and few systems capable of real-time transient detection have ever been implemented. This research addresses the need for an online monitoring framework aimed towards understanding pressure transient effects and behaviour. The proposed system uses an Internet of Things approach, combining pressure sensors with Raspberry Pi computers, as well as open-source tools that transmit and display the data. The data analysis combines computationally inexpensive methods in order to achieve an accurate decision-making tool for both transient detection and abnormal transient risk identification. The techniques used include different filtering and detrending methods, feature extraction for dimensionality reduction, three-sigma statistical process control, and classification using voting methods. The process also includes a second process, based on statistical process control and trained using transient data identified in the original process, in order to assign a risk for a transient to cause damage, as well as identify transients that are particularly severe. Data was collected from a unique laboratory water distribution network as well as a field installation in Guelph, Ontario. The results showed that the framework achieves real-time transient identification with reasonable detection and error rates. Further analysis illustrated the effect of factors such as transient source location, active flow in the pipes, and transient type, on transient propagation and detection. The performance of the framework proves the concept of IoT-based systems for pressure monitoring and event detection in municipal water infrastructure

Sensor placement for fault location identification in water networks: A minimum test cover approach

This paper focuses on the optimal sensor placement problem for the identification of pipe failure locations in large-scale urban water systems. The problem involves selecting the minimum number of sensors such that every pipe failure can be uniquely localized. This problem can be viewed as a minimum test cover (MTC) problem, which is NP-hard. We consider two approaches to obtain approximate solutions to this problem. In the first approach, we transform the MTC problem to a minimum set cover (MSC) problem and use the greedy algorithm that exploits the submodularity property of the MSC problem to compute the solution to the MTC problem. In the second approach, we develop a new \textit{augmented greedy} algorithm for solving the MTC problem. This approach does not require the transformation of the MTC to MSC. Our augmented greedy algorithm provides in a significant computational improvement while guaranteeing the same approximation ratio as the first approach. We propose several metrics to evaluate the performance of the sensor placement designs. Finally, we present detailed computational experiments for a number of real water distribution networks