Neural net versus classical models for the detection and localization of leaks in pipelines

D. Matko, G. Geiger and T. Werner

Mathematical and Computer Modelling of Dynamical Systems, 2006, vol. 12, issue 6, 505-517

Abstract: Four models of a pipeline are compared in the paper: a nonlinear distributed-parameter model, a linear distributed-parameter model, a simplified lumped-parameter model and an extended neural-net-based model. The transcendental transfer function of the linearized model is obtained by a Laplace transformation and corresponding initial and boundary conditions. The lumped-parameter model is obtained by a Taylor series extension of the transencdental transfer function. Based on the experience of linear models the structure of the neural net model, as an addendum to the nonlinear distributed-parameter model, is obtained. All four models are tested on a real pipeline data with an artificially generated leak.

Date: 2006
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DOI: 10.1080/13873950500068526

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