Using an artificial neural network to evaluate the hull condition of naval vessels

Abstract

The evaluation of the hull condition of naval vessels is a crucial part for corrosion protection systems due to the direct linkage between the electrochemical process at the hull/water interface leading to corrosion and the overall coating of the hull to prevent the corrosion process. In the case of the latter, the condition is unknown while the vessel is on a mission and either has to be evaluated by divers (in open water) or on dry docks which is a time consuming process, respectively. In our work, we present a methodology to localize coating damages without the need of divers or dry docks using an artificial neural network (ANN) combined with the information provided by the onboard impressed current cathodic protection (ICCP) system to predict said damages in a specific sector of a generic ship model. Using only the ICCP currents as highly aggregated input variables for the ANN, approximately 86 % of randomly sized and positioned coating damages are correctly predicted.