期刊
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
卷 140, 期 4, 页码 457-467出版社
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)WR.1943-5452.0000339
关键词
Water distribution systems; Bayesian inference systems; Wavelets; Artificial neural networks; Statistical process control; Pipe bursts detection
资金
- University of Exeter
- U.K. Engineering and Physical Sciences Research Council [EP/E003192/1]
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.
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