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A common method for leak localization in water pipelines is the use of cross correlation on two measured acoustic signals located on either side of a leak. However, in practice, leak signals are inevitably corrupted with non-leak sounds and noise. This is due to the complicated pipeline environment that makes time delay estimation between the sensors inaccurate. In addition, time delay errors further affect leak localization accuracy, and then reduce the reliability of the system. In this paper, a new adaptive leak detection method that combines BP neural networks with GCC (generalized cross correlation) is proposed. The new proposed method can discriminate the leak signal from non-leak acoustic sources and noise, and improve leak localization accuracy. Simulation results indicates that the proposed leak detection method can be used to localize a leak in a buried water pipeline and achieved a correct detection rate of 92.5%.

An adaptive method for water pipeline leak localization