Multiscale processing of loss of metal: A machine learning approach

Abstract

Corrosion is one of the principal causes of degradation to failure of marine structures. In practice, localized corrosion is the most dangerous mode of attack and can result in serious failures, in particular in marine flowlines and inter-field lines, arousing serious concerns relatively to environmental impact. The progress in time of internal corrosion, the location along the route and across the pipe section, the development pattern and the depth of the loss of metal are a very complex issue: the most important factors are products characteristics, transport conditions over the operating lifespan, process fluid-dynamics, and pipeline geometrical configuration. Understanding which factors among them play the most important role is a key step to develop a model able to predict with enough accuracy the sections more exposed to risk of failure. Some factors play a crucial role at certain spatial scales while other factors at other scales. The Mutual Information Theory, intimately related to the concept of Shannon Entropy in Information theory, has been applied to detect the most important variables at each scale. Finally, the variables emerged from this analysis at each scale have been integrated in a predicting data driven model sensibly improving its performance.