Leak Detection and Topology Identification in Pipelines Using Fluid Transients and Artificial Neural Networks

Abstract

Condition assessment of water pipelines using fluid transient waves is a noninvasive technique that has been investigated for the past 25 years. Approaches to identify different anomalies and to identify elements of the topology of a pipeline have been proposed but often require detailed modeling and knowledge of the system. On the other hand, artificial neural networks (ANN) have become a useful tool in a range of different fields by enabling a computer to solve a problem without being explicitly programmed to do so, but rather by learning from a series of known examples. This paper presents a new methodology that uses ANNs to predict the presence of features in a pipeline. First, the location and characteristics of a junction have been predicted as a way to identify elements of the topology of a pipeline followed by identification of the location and sizing of a leak. The ANN characteristics and training approaches have been determined for both the junction and the leak example. Results show that the ANN that has been designed for this research is able to accurately predict the location of a junction with an error in this estimation of 2.32 m (out of a 1,000 m long pipeline) or less in 95% of the tested cases. The prediction of the two different diameters on either side of the junction was extremely accurate with only one misidentification of one of the diameters in the 5,000 tested examples. When the ANN was trained and tested to locate and size a leak, the results were also successful. A total of 95% of the tested examples located the leak with an error equal or less than 3.0 m (out of a 1,000 m pipe length) and the leak size was predicted with an average absolute error of only 0.31 mm. The results presented in this paper demonstrate the potential of combining the use of both fluid transient pressure waves and ANNs for the detection of features in pipelines.