A Neural Model to Detect and Determine the Magnitude of Leaks in Gas Pipelines

Rejane B. Santos, Elisangela O. de Sousa, Sandra L. da Cruz, and Ana Maria F. Fileti


pipeline, leak, neural network


Considering the importance of monitoring piping systems, the aim of this work is to develop a technique to detect gas leaks in pipes, based on acoustic method, in order to determining the magnitude of leaks through the use of neural artificial networks. Audible noise, generated by leaks, was detected in a pipe 100 m long. The experimental data, obtained through a microphone installed inside the pressure vessel and connected to a data acquisition system, are decomposed into sounds of different frequencies. The dynamics of these noises in time is used as input to the neural model to determine the occurrence and magnitude of the leaks. From the results, it was observed that the leaks were properly detected by the acoustic method in all situations. The developed neural models determined successfully the magnitude of new leaks caused in the same pipe.

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