Title :
An Approach to Leak Detection in Pipe Networks Using Analysis of Monitored Pressure Values by Support Vector Machine
Author :
Mashford, John ; De Silva, Daswin ; Marney, Donavan ; Burn, Stewart
Author_Institution :
Commonwealth Sci. & Ind. Res. Organ., Highett, VIC, Australia
Abstract :
This paper presents a method of mining the data obtained by a collection of pressure sensors monitoring a pipe network to obtain information about the location and size of leaks in the network. This inverse engineering problem is effected by support vector machines (SVMs) which act as pattern recognisers. In this study the SVMs are trained and tested on data obtained from the EPANET hydraulic modelling system. Performance assessment of the SVM showed that leak size and location are both predicted with a reasonable degree of accuracy. The information obtained from this SVM analysis would be invaluable to water authorities in overcoming the ongoing problem of leak detection.
Keywords :
data mining; leak detection; pattern recognition; pipelines; pressure sensors; support vector machines; EPANET hydraulic modelling system; data mining; inverse engineering; leak detection; monitored pressure values; pipe networks; pressure sensors; support vector machine; water authorities; Artificial neural networks; Condition monitoring; Data mining; Information analysis; Leak detection; Pattern recognition; Sensor systems; Signal analysis; Support vector machines; Temperature sensors; leak detection; pattern recognition; pipe networks; support vector machines;
Conference_Titel :
Network and System Security, 2009. NSS '09. Third International Conference on
Conference_Location :
Gold Coast, QLD
Print_ISBN :
978-1-4244-5087-9
Electronic_ISBN :
978-0-7695-3838-9
DOI :
10.1109/NSS.2009.38
