Measurement error sensitivity analysis for detecting and locating leak in pipeline using ANN and SVM

Mohammad Tariq Nasir, Muhammad Mysorewala, Lahouari Cheded, Bilal Siddiqui, Muhammad Sabih

Research output: Contribution to conferencePaperpeer-review

25 Scopus citations

Abstract

This paper presents an approach for detecting, locating and estimating the size of leak in a pipeline using pressure sensors, differential pressure sensors and flow-rate sensors. To overcome the problem with existing approaches we use differential pressure sensors that detect small change in pressure in order to detect small change in leak size. The pipeline system is modeled and simulated in EPANET software, and the input-output data acquired from it (i.e. sensor measurements and the leak locations and sizes) are used in MATLAB and DTREG software to develop Artificial Neural Network (ANN) and Support Vector Machines (SVM) models. Comparison of results shows that SVM is less sensitive and more stable to noise increment than ANN. However the performance of ANN is better with very small noises.

Original languageEnglish
DOIs
StatePublished - 2014

Keywords

  • Artificial neural network
  • Leak detection and localization
  • Pipeline
  • Support vector machines

ASJC Scopus subject areas

  • Signal Processing
  • Control and Systems Engineering

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