Damage detection of bottom-set gillnet using Artificial Neural Network

Abstract

This paper presents a method for detecting damage of a bottom-set gillnet based on sensor fusion and the Artificial Neural Network (ANN) model. In this regard, time-domain numerical simulations for a 300-m-long bottom-set gillnet with an equivalent-drag-net model are extensively performed. Various wave conditions as well as numerous damaged scenarios are considered in the numerical simulations, and extensive data are collected for the training and testing of the ANN-based machine-learning scheme. In training, representative sea states, net-assembly accelerations, and location-buoy displacements are selected as the input variables. The back-propagation learning algorithm is employed for training to maximize the damage-detection performance. The output of the ANN model is the identification of the type and location of the damaged net. The damage-detection capability is significantly enhanced by employing the moving standard deviation and median filter. The well-trained ANN models are shown to accurately (96% correct) detect the damage of the net even for the sea states not included in training. This study demonstrates that the real-time automatic monitoring of the underwater net systems can be designed by using the digital-twins technology with the applied ANN model and machine-learning scheme for the detection of damages and malfunctions.