NDLI: An Approach to Leak Detection in Pipe Networks Using Analysis of Monitored Pressure Values by Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Mashford, J. De Silva, D. Marney, D. Burn, S.
Copyright Year	2009
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.
Starting Page	534
Ending Page	539
File Size	476469
Page Count	6
File Format	PDF
ISBN	9781424450879
DOI	10.1109/NSS.2009.38
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-19
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Condition monitoring Support vector machines Temperature sensors Artificial neural networks Pipe networks Sensor systems Pattern recognition Data mining Leak detection Signal analysis Information analysis
Content Type	Text
Resource Type	Article

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An Approach to Leak Detection in Pipe Networks Using Analysis of Monitored Pressure Values by Support Vector Machine