Coating damage localization of naval vessels using artificial neural networks

被引:11
作者
Thiel, Christian [1 ,2 ]
Neumann, Kevin [1 ,2 ]
Ludwar, Frank [3 ]
Rennings, Andreas [1 ,2 ]
Doose, Jens [3 ]
Erni, Daniel [1 ,2 ]
机构
[1] Univ Duisburg Essen, Gen & Theoret Elect Engn ATE, D-47048 Duisburg, Germany
[2] CENIDE Ctr Nanointegrat Duisburg Essen, D-47048 Duisburg, Germany
[3] Bundeswehr, Tech Ctr Ships & Naval Weap Naval Technol & Res W, D-24340 Eckernforde, Germany
关键词
Cathodic protection; Artificial neural network; Machine learning; ICCP; UEP; SACP;
D O I
10.1016/j.oceaneng.2019.106560
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
For the localization of coating damages of naval vessels numerical simulation using the FEM software COMSOL Multiphysics were carried out to calculate the corresponding underwater electric potential (UEP) signature. Therefore, we defined said damages at random hull surface positions and used the information provided by the impressed current cathodic protection (ICCP) system, more exactly the cathodic current itself, and the calculated UEP signatures as input parameters to train an artificial neural network (ANN) for predicting the coating damage location. With this deep learning approach, more than 90% of all coating damages are predicted correctly, considering a generic ship model with 50 m length, whose hull is divided into 12 different sectors. Even the mere use of ICCP currents as highly aggregated input parameters for the ANN lead to a satisfactory prediction rate over 80% within the predefined sectors, thus providing quite accurate results using minimal amount of data.
引用
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页数:7
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