Disturbance Extraction for Burst Detection in Water Distribution Networks Using Pressure Measurements
Weirong Xu, Xiao Zhou, Kunlun Xin, Joby Boxall, Hexiang Yan, Tao Tao
Abstract
Abstract Pipe bursts in water distribution networks cause considerable water losses and lead to potential environmental hazards. Effective burst detection methods enable water companies to repair broken pipes in a timely manner and minimize damage and disruption. A data‐driven detection method is developed and proven for real‐time leak and burst detection in water distribution networks. The method uses a unique integration of disturbance extraction and isolation forest techniques to enable detection of subtle burst signals from normally noisy pressure data. Verification and validation progress from synthetic data to the application to real‐life data from large‐scale open networks. The method is shown to generate comparable levels of detection, with low false positive rates, to other published techniques but without the need to introduce closed valves and expensive flow meters of district meter area structures and the loss of resilience these cause. The method offers the potential to effectively detect bursts from pressure data alone, helping to better manage the distribution of increasingly stressed water resources through aging pipe networks.