Neural Network Precept Diagnosis on Petrochemical Pipelines for Quality Maintenance

Abstract

Pipeline tubes are part of vital mechanical systems largely used in petrochemical industries. They serve to transport natural gases or liquids. They are cylindrical tubes and are submitted to the risks of corrosion due to high PH concentrations of the transported liquids in addition to fatigue cracks. Due to the nature of their function, they are subject to the alternation of pressure-depression along the time, initiating therefore in the tubes' body micro-cracks that can propagate abruptly to lead to failure by fatigue. On to the diagnostic study for the issue the development of this prognostic process employing neural network for such systems bounds to the scope of quality maintenance.

Keywords: Percept, Simulated results, Fluid Mechanics