Low-cost failure sensor design and development for water pipeline distribution systems.

This paper describes the design and development of a new sensor which is low cost to manufacture and install and is reliable in operation with sufficient accuracy, resolution and repeatability for use in newly developed systems for pipeline monitoring and leakage detection. To provide an appropriate signal, the concept of a "failure" sensor is introduced, in which the output is not necessarily proportional to the input, but is unmistakably affected when an unusual event occurs. The design of this failure sensor is based on the water opacity which can be indicative of an unusual event in a water distribution network. The laboratory work and field trials necessary to design and prove out this type of failure sensor are described here. It is concluded that a low-cost failure sensor of this type has good potential for use in a comprehensive water monitoring and management system based on Artificial Neural Networks (ANN).

A neural network approach to burst detection.

This paper describes how hydraulic and water quality data from a distribution network may be used to provide a more efficient leakage management capability for the water industry. The research presented concerns the application of artificial neural networks to the issue of detection and location of leakage in treated water distribution systems. An architecture for an Artificial Neural Network (ANN) based system is outlined. The neural network uses time series data produced by sensors to directly construct an empirical model for predication and classification of leaks. Results are presented using data from an experimental site in Yorkshire Water's Keighley distribution system.