Pipe bursts in a drinking water distribution system lead to water losses, interruption of supply, and damage to streets and houses due to the uncontrolled water flow. To minimize the negative consequences of pipe bursts, an early detection is necessary. This paper describes a heuristic burst detection method, which continuously compares forecasted and measured values of the water demand. The forecasts of the water demand were generated by an adaptive water demand forecasting model. To test the method, a dataset of five years of water demand data in a supply area in the Western part of the Netherlands was collected. The method was tested on a subset of the data (only the winter months) in which 9 (larger) burst events were reported. The detection probability for the reported bursts was 44.4%, at an acceptable rate of false alarms of 5.0%. The results were compared with the CUSUM method, which is a general statistical process control (SPC) method to identify anomalies in time series. The heuristic and CUSUM methods generated comparable results, although rate of false alarm for the heuristic method was lower at the same detection probability.
|Number of pages||8|
|Publication status||Published - 2014|
|Event||12th International Conference on Computing and Control for the Water Industry, CCWI 2013 - Perugia, Italy|
Duration: 2013 Sept 2 → 2013 Sept 4
Bibliographical noteFunding Information:
This study was carried out in the DisConTO project (Distribution Control Training & Operation). The project is a cooperation between 4 water supply companies (Vitens, Dunea, PWN and Brabant Water), Delft University of Technology, The National Institute for Public Health and the Environment (RIVM), Royal HaskoningDHV Consultancy and Engineering and UReason. The project is financially supported by the Dutch government through the “Innowator” program.
- Demand forecasting
- Pipe burst detection
- SPC methods
ASJC Scopus subject areas