期刊
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
卷 140, 期 4, 页码 417-424出版社
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)WR.1943-5452.0000344
关键词
Signal processing; Flow; Unsupervised classification; Burst detection; Water distribution system; Weighted least squares; Expectation maximization algorithm
资金
- EPSRC [EP/E003192/1]
- EPSRC [EP/E003192/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/E003192/1] Funding Source: researchfish
Flow measurement data at the district meter area (DMA) level has the potential for burst detection in the water distribution systems. This work investigates using a polynomial function fitted to the historic flow measurements based on a weighted least-squares method for automatic burst detection in the U.K. water distribution networks. This approach, when used in conjunction with an expectation-maximization (EM) algorithm, can automatically select useful data from the historic flow measurements, which may contain normal and abnormal operating conditions in the distribution network, e.g.,water burst. Thus, the model can estimate the normal water flow (nonburst condition), and hence the burst size on the water distribution system can be calculated from the difference between the measured flow and the estimated flow. The distinguishing feature of this method is that the burst detection is fully unsupervised, and the burst events that have occurred in the historic data do not affect the procedure and bias the burst detection algorithm. Experimental validation of the method has been carried out using a series of flushing events that simulate burst conditions to confirm that the simulated burst sizes are capable of being estimated correctly. This method was also applied to eight DMAs with known real burst events, and the results of burst detections are shown to relate to the water company's records of pipeline reparation work.
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