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

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

Artificial neural network

Artificial neural networks

Computational modeling

Leak detection and localization

MATLAB

Noise

Pipeline

Sensor systems

Support vector machines

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.

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M. T. Nasir

M. Mysorewala

L. Cheded

B. Siddiqui

M. Sabih

2014 IEEE 11th International Multi-Conference on Systems, Signals & Devices (SSD14)

2014

conferencePaper