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The increasing age and deterioration of drinking water mains is causing an increasing frequency of pipe bursts. Not only are pipe repairs costly, bursts might also lead to contamination of the Dutch non-chlorinated drinking water, as well as damage to other above-and underground infrastructure. Detection and localization of pipe bursts have long been priorities for water distribution companies. Here we present a method for proactive leakage control, referred to as Monitoring Support. Contrary to most leak prevention methods, our method is based on real-time pressure sensor measurements and focuses on detection of recurring pressure anomalies, which are assumed to be indicative of misuse or malfunctioning of the water distribution network. The method visualizes and warns for both recurring and one-time anomalous events and offers monitoring experts an unsupervised decision support tool that requires no training data or manual labeling. Additionally, our method supports any time series data source and can be applied to other types of distribution networks, such as those for gas, electricity and oil. The performance of our method, including both instance-based and featurebased clustering, was validated on two pressure sensor data sets. Results indicate that featurebased clustering is the best method for detection of recurring pressure anomalies, with accuracy F 1-scores of 92% and 94% for a 2013 and 2017 data set, respectively.
Bursts of drinking water pipes not only cause loss of drinking water, but also damage below and above ground infrastructure. Short-term water demand forecasting is a valuable tool in burst detection, as deviations between the forecast and actual water demand may indicate a new burst. Many of burst detection methods struggle with false positives due to non-seasonal water consumption as a result of e.g. environmental, economic or demographic exogenous influences, such as weather, holidays, festivities or pandemics. Finding a robust alternative that reduces the false positive rate of burst detection and does not rely on data from exogenous processes is essential. We present such a burst detection method, based on Bayesian ridge regression and Random Sample Consensus. Our exogenous nowcasting method relies on signals of all nearby flow and pressure sensors in the distribution net with the aim to reduce the false positive rate. The method requires neither data of exogenous processes, nor extensive historical data, but only requires one week of historical data per flow/pressure sensor. The exogenous nowcasting method is compared with a common water demand forecasting method for burst detection and shows sufficiently higher Nash-Sutcliffe model efficiencies of 82.7% - 90.6% compared to 57.9% - 77.7%, respectively. These efficiency ranges indicate a more accurate water demand prediction, resulting in more precise burst detection.
Conduit bursts or leakages present an ongoing problem for hydraulic fluid transport grids, such as oil or water conduit networks. Better monitoring allows for easier identification of burst sites and faster response strategies but heavily relies on sufficient insight in the network’s dynamics, obtained from real-time flow and pressure sensor data. This paper presents a linearized state-space model of hydraulic networks suited for optimal sensor placement. Observability Gramians are used to identify the optimal sensor configuration by maximizing the output energy of network states. This approach does not rely on model simulation of hydraulic burst scenarios or on burst sensitivity matrices, but, instead, it determines optimal sensor placement solely from the model structure, taking into account the pressure dynamics and hydraulics of the network. For a good understanding of the method, it is illustrated by two small water distribution networks. The results show that the best sensor locations for these networks can be accurately determined and explained. A third example is added to demonstrate our method to a more realistic case.
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