Techno Press
Techno Press

Structural Monitoring and Maintenance   Volume 3, Number 3, September 2016, pages 297-314
FE and ANN model of ECS to simulate the pipelines suffer from internal corrosion
Wael A. Altabey

Abstract     [Full Text]
    As the study of internal corrosion of pipeline need a large number of experiments as well as long time, so there is a need for new computational technique to expand the spectrum of the results and to save time. The present work represents a new non-destructive evaluation (NDE) technique for detecting the internal corrosion inside pipeline by evaluating the dielectric properties of steel pipe at room temperature by using electrical capacitance sensor (ECS), then predict the effect of pipeline environment temperature (o) on the corrosion rates by designing an efficient artificial neural network (ANN) architecture. ECS consists of number of electrodes mounted on the outer surface of pipeline, the sensor shape, electrode configuration, and the number of electrodes that comprise three key elements of two dimensional capacitance sensors are illustrated. The variation in the dielectric signatures was employed to design electrical capacitance sensor (ECS) with high sensitivity to detect such defects. The rules of 24-electrode sensor parameters such as capacitance, capacitance change, and change rate of capacitance are discussed by ANSYS and MATLAB, which are combined to simulate sensor characteristic. A feed-forward neural network (FFNN) structure are applied, trained and tested to predict the finite element (FE) results of corrosion rates under room temperature, and then used the trained FFNN to predict corrosion rates at different temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an FFNN results, thus validating the accuracy and reliability of the proposed technique and leads to better understanding of the corrosion mechanism under different pipeline environmental temperature.
Key Words
    Electrical Capacitance Sensor (ECS); internal corrosion detection; artificial neural; network (ANN)
Wael A. Altabey: International Institute for Urban Systems Engineering, Southeast University, Nanjing (210096), China;
Department of Mechanical Engineering, Faculty of Engineering, Alexandria University, Alexandria (21544), Egypt

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