Detection of damaged mooring line based on deep neural networks

Abstract

Since severe damage to the floating offshore structures due to the deterioration of their structural stability may lead to major disasters, it is necessary to detect mooring line damage at an early stage. However, most of the existing damage detection approaches of mooring line have difficulties to provide constant monitoring or to detect local damages to line. This study aims to develop a detection approach of a damaged mooring line in tension leg platform (TLP) based on deep neural networks (DNN). Simulation data with Charm3D was used for training and testing the DNN in the study because it is impractical to obtain actual data by intentionally damaging mooring lines that are in operation. The accuracies of the DNN model were significantly high (94.6%–99.3%) with noise level differing from 0% to 20%. The quite low false negative (FN) errors of 0.7%–5.4% for noise levels of 1–20% shows the potential of DNN-based structural health monitoring system to identify a damaged mooring line in TLP. The results of the study indicate that DNN-based damage detection approach with floater responses is applicable for even a local damage, and thus can prevent further damage or accident by early-stage detection.