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Artificial Neural Network Model for a Low Cost Failure Sensor: Performance Assessment in Pipeline Distribution

Authors: Asar Khan, Peter D. Widdop, Andrew J. Day, Aliaster S. Wood, Steve, R. Mounce, John Machell

Abstract:

This paper describes an automated event detection and location system for water distribution pipelines which is based upon low-cost sensor technology and signature analysis by an Artificial Neural Network (ANN). The development of a low cost failure sensor which measures the opacity or cloudiness of the local water flow has been designed, developed and validated, and an ANN based system is then described which uses time series data produced by sensors to construct an empirical model for time series prediction and classification of events. These two components have been installed, tested and verified in an experimental site in a UK water distribution system. Verification of the system has been achieved from a series of simulated burst trials which have provided real data sets. It is concluded that the system has potential in water distribution network management.

Keywords: Detection, leakage, neural networks, sensors, water distribution networks

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1055579

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References:


[1] O. Hunaidi, W. Chu, A. Wang, and W. Guan, "Leak detection methods for plastic water distribution pipes, Advancing the Science of Water", Fort Lauderdale Technology Transfer Conference, AWWA Research Foundation, 1999, 249-270, Florida.
[2] M. Martin and L. Farley, "Improving water management in leakage control", Proceedings of Hydrotop, 1994, Marseille, France.
[3] J. Grolby and T. Woodward, "Find that leak", IEE Review, September 1999.
[4] G. Bridges and M. MacDonald, "Leakage controlÔÇöthe neglected solution?", 20th WEDC Conference, Colombo, 1994, Sri Lanka.
[5] A. Khan, P. D. Widdop, A. J. Day, A. S. Wood, S. R. Mounce, and J.Machell, "Low-cost failure sensor systems for water pipeline distribution systems", Water Science and Technology, 2002, Vol. 45 (4/5) 207-216.
[6] C. M. Bishop, "Mixture density networks", Technical report NCRG/94/004, Department of Computer Science and Applied Mathematics, Aston University, Birmingham, 1994, UK.
[7] S. R. Mounce, A. J. Day, A. S. Wood, A. Khan, P. D. Widdop, J.Machell, "A neural network approach to burst detection", Water Science and Technology, 2002, Vol. 45 (4/5) 237-246.
[8] G. J. McLachlan and K. E. Basford, "Mixture models: Inference and Applications to Clustering", New York: Marcel Dekker, 1988.
[9] D. E. Rumelhart, G. E. Hinton and R. J. Williams, "Learning representations of back-propagation errors", Nature (London), 1986, Vol. 323, pp. 533-536.