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

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  • D. Matko
  • G. Geiger
  • T. Werner

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.

Suggested Citation

  • D. Matko & G. Geiger & T. Werner, 2006. "Neural net versus classical models for the detection and localization of leaks in pipelines," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 12(6), pages 505-517, December.
    • Handle: RePEc:taf:nmcmxx:v:12:y:2006:i:6:p:505-517
    DOI: 10.1080/13873950500068526
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