Automated Detection of Pipe Bursts and Other Events in Water Distribution Systems

This paper presents a new methodology for the automated near-real-time detection of pipe bursts and other events that induce similar abnormal pressure/flow variations (e.g., unauthorized consumptions) at the district metered area (DMA) level. The new methodology makes synergistic use of several self-learning artificial intelligence (AI) techniques and statistical data analysis tools, including wavelets for denoising of the recorded pressure/flow signals, artificial neural networks (ANNs) for the short-term forecasting of pressure/flow signal values, statistical process control (SPC) techniques for short- and long-term analysis of the pipe burst/other event-induced pressure/flow variations, and Bayesian inference systems (BISs) for inferring the probability of a pipe burst/other event occurrence and raising corresponding detection alarms. The methodology presented here is tested and verified on a case study involving several DMAs in the United Kingdom (U.K.) with both real-life pipe burst/other events and engineered (i.e., simulated by opening fire hydrants) pipe burst events. The results obtained illustrate that it can successfully identify these events in a fast and reliable manner with a low false alarm rate.